Tuning My Pump Settings With AndroidAPS and Nightscout

This is going to be an interesting week. My Omnipod subscription is about to run out and I am getting an Ypsopump at the end of the week. I love Omnipod but, as it is completely unsubsidized/insured in Australia, my hip pocket dictates I need to move to something else. This means I will be moving from AndroidAPS to CamAPS and, in the intervening days, I am going to run AndroidAPS with MDI to see what that looks like.

I assume it means the looping engine will give me recommendations for injections, rather than automatically applying them.

I am going to miss AndroidAPS as it has been excellent. With my settings tuned, the loop was completely closed; I did not declare meals and yet I still maintained an HbA1c of 6.0% and a Time in Range of around 90%. I avoid overly carby food but I am far from a low carb regimen. A completely closed loop will not be possible with CamAPS but I do hear it is excellent.

Before I leave AndroidAPS though I thought I would document how I tuned my settings in case others were looking to do so and so, when I return to AndroidAPS, I will have this as a reminder of what I did.

Tuning So Far

In a couple of previous blogs, I talked about how I found things like my basal rate, and pump settings through finger pricking and CGM use. Here I use the full power of AndroidAPS and Nightscout to analyse my CGM data across multiple days for a much more accurate result. Before I launch into my method, I should explain what AndroidAPS and Nightscout are.

AndroidAPS is an open source looping system adapted from OpenAPS and made specifically for Android phones. I spoke about it previous here in my tech/CRM blog.

Nightscout is, effectively, an online website with your CGM data in it. I have been running it for years now and, if you are wanting more information on it and how to set it up, you can find it here. As well as showing CGM data, AndroidAPS can also push up data from AndroidAPS. For me it was a great way to have all of my data online so I could do things like access it via Zapier or Power Automate. In fact there is a lot you can do with the Nightscout data. I previously mentioned how I use it to generate reports for my endocrinologist. It turns out you can also use to to tune your pump settings via Autotune.

Autotune

The easiest way to access Autotune is via Mark Carrington’s online interface at https://autotuneweb.azurewebsites.net/.

Here we put in our Nightscout URL and tune the values of interest.

In my basal profile I have six values covering four hours each so I often run Autotune six times to cover each time period.

The result is a set of recommendations for the basal rate per hour, the value for the Insulin-Carb ratio and the Insulin Sensitivity Factor.

The problem with all of this is how it comes up with these recommendations is not clear so I prefer to compare it to my own conclusions before implementing.

Comparing the Recommendations to Observation

AndroidAPS allows you to display a lot of useful graphs which become meaningful once you start using the system,

The first graph here is the blood glucose level (yes, it had been quite an interesting 24 hours due to business travel, airport lounges, and all you can eat party pies). The second called “IOB COB” shows the Insulin on Board (IOB). In the case of AndroidAPS this considers basal and bolus insulin and can go negative. Negative IOB means the loop reduced insulin delivery due to a pending low for a sufficiently long time as to affect the usual blood insulin level for basal insulin. A sustained negative IOB can imply that the basal rates are set too high. In the above case the IOB is positive in all six time periods which suggests perhaps the rate is too low.

The second graph shows insulin sensitivity with the white line or, more accurately insulin resistance as a high value means more resistant. The green/yellow/red bars indicate deviations from the expected response of insulin to a carbohydrate intake. In the case of positive values in the deviation it means the insulin was not as effective as expected i.e. the ratio is incorrect.

Running the Comparison of the Graphs to AutoTune

I manage the comparison in Excel. I take values over three days and use Excel formula to compare.

If both my conclusion and Autotune agree with each other, I accept Autotune’s recommendation. Otherwise I leave the value alone.

In the above tables we see that, for this particular period, there was an adjustment made to a couple of time periods for my basal rate, a couple for my insulin sensitivity factor, and no change for my insulin-carbohydrate ratio.

Conclusions

The ability to tune my pump settings with Autotune’s analysis is really powerful and has given me something which is practically a closed loop system. However, as I do not know how it comes up with the recommendations, I prefer not to completely hand over control to the machine. To this end I do my own analysis and move slowly. The three day review removes outliers from any one day (such as crazy numbers from a business trip) and allows for trends to reveal themselves. Working in partnership with the machine has proven to be very powerful and I hope I can achieve similar outcomes with the commercial CamAPS.

ISPAD 2022: What We Know About Type 1 Diabetes May Be Upside Down

Last year, when I attended ATTD 2021, I wrote about Dr. Walter Pories and his observations in people with type 2 diabetes undergoing gastric bypass surgery. You can read that article here but, in short, obese people with type 2 diabetes, immediately after gastric bypass surgery, begin seeing significant improvements in insulin resistance/requirements. Literally in the days following surgery insulin requirement drops by a factor of 10 and, within a month, remission often occurs. Now the surgery does not remove the fat, visceral or otherwise, and yet there is such a dramatic improvement in insulin needs.

Dr. Pories concluded type 2 diabetes was not caused by “lifestyle” or visceral fat deposits but by a faulty gut which processed food poorly generating excess fat and little energy. The net result is increased weight, low energy levels, and overeating to compensate generating a vicious cycle. This subsequently strains the pancreas in certain individuals whose pancreas cannot keep up and we have type 2 diabetes.

Rather than being a disease of the lazy or a disease of the gluttonous, if Dr Pories is right, type 2 diabetes is a disease of the gut.

This month I was fortunate enough to attend ISPAD 2022 as a dedoc Voice and it turns out there is a controversy with type 1 diabetes which also challenges the cause.

Conventional Wisdom On The Cause Of Type 1 Diabetes.

The conventional thinking on the cause of type 1 diabetes begins with what we can observe and that is the immune system. Generally speaking, in people with type 1 diabetes, we see auto-antibody markers. In other words, the body’s immune system is attacking the pancreas and, specifically, the beta cells.

The idea often put forward is an “environmental factor” interacts with the body somehow, and the immune system responds to what it believes is a threat. As the immune system is a learning system, it remembers the threat and is prepared to attack it in the future. The theory is that the threat looks similar to the beta cells in the body and so the immune system begins attacking them, leading to depletion and type 1 diabetes.

That environmental factor might be a virus, or it could be gluten causing a “leaky gut”, exposing the immune system to food particles and confusing it. Professor Bart Roep is suggesting pinning it on the “environmental factor” is missing the mark.

What If The Beta Cells Were The Problem?

Professor Roep gave his talk in the first session of the first day and set quite the benchmark.

He is looking at how we are approaching cancer treatment and whether the lessons learned there can be used for the treatment of type 1 diabetes. Rather than seeing type 1 diabetes as caused by a confused immune system, he suggests it is caused by stressed beta cells.

To explain this he cited the late Gian Franco Bottazzo who promoted a radical idea; rather than the immune system causing indiscriminate destruction of beta cells (homicide), the beta cells are literally asking to be killed (as also happens with cancer cells) and the immune system is obliging.

Rebalancing The Immune System

When I was first diagnosed with type 1 diabetes, and told it was a problem of the immune system, my first thought was to turn to immune suppression drugs, as we do with organ transplants to prevent the immune system from attacking the new organ. So why do we not use immune suppressions drugs to slow the destruction of the pancreas? Because, in this case, the treatment is considered worse than the disease.

There are significant side effects from immune suppression drugs not the least of which is an increased risk of cancer. It turns out the body’s cells know they are cancerous or infected by a virus and literally put their hand up to be killed by the immune system. When we suppress the immune system this goes unchecked; infections and cancers are left unhindered and compromise the host.

On the other side of the coin, if we boost the immune system, this can trigger a disease very similar to type 1 diabetes.

The idea being that the immune system becomes much more sensitive to stressed cells and attacks cells with less discrimination. Great for cancer but not so great for beta cells.

Beta Cells Are Stressed

So why are beta cells the outlier in being attacked? Why does the immune system focus here and not on the entire body? It turns out beta cells are some of the hardest working cells in the human body producing literally a million copies of insulin every minute.

It therefore makes sense that it would not take a lot to tip a beta cell over the stress threshold and be targeted by the immune system.

Evidence That Stress Is The Problem

LADA forums frequently advise that to prolong the honeymoon (time when beta cells are still producing substantial amounts of insulin), you must reduce the stress on the beta cells. Often recommendations include dietary adjustments, such as eating low carbohydrate, supplements to reduce inflammation, and early-intervention insulin to reduce the requirement of the beta cells.

Even in the medical literature there is evidence (or, at least, consistency) that beta cell stress accelerates the destruction of beta cells. In “Latent Autoimmune Diabetes in Adults: A Review on Clinical Implications and Management”, a summary of findings from other studies included:

  • Insulin sensitizers plus insulin therapy preserve beta cells function better than insulin alone
  • Progression to insulin dependence was slower when insulin therapy was used compared to the use of sulfonylureas (a class of drugs which force beta cells to produce more insulin)

“Beta-Cell Preservation…Is Weight Loss the Answer?” presents evidence that weight loss can lower insulin resistance and preserve beta cell function.

All of the above findings are consistent with this stress model for Type 1 diabetes.

Similarly, this model does not exclude things like viruses as being involved, it simply recasts their role as stressors of the beta cells rather than antagonists of the immune system.

The Opportunity For New Treatments

Looking at cancer treatment breakthroughs and applying an inverse approach we get a novel set of approaches for curing/managing type 1 diabetes.

CAR T cells

In cancer, the immune cells (T cells) are reengineered to attack cancer cells. For type 1 diabetes, the reengineering would be with the cells that regulate the T cells (Regulatory T cells) to supress the immune response specifically for beta cell destruction.

Bionic: therapeutic Ab conjugated with toxin

In cancer, antibodies are engineered to carry a toxin so when they attack the cancer cell they deliver a toxic payload to wipe out the cancel cell. This same technique would be used but to deliver a growth factor to revitalise the beta cell.

Reduce hypo inflammatory tumor environment

Cancer cells actively reduce inflammation to limit the immune response. Treatments often seek to reverse this. In the case of type 1 diabetes, treatments would look to enhance the low inflammatory state.

DC vaccination (Dendreon)

In cancer, a company called Dendreon has a vaccine which selectively activates the immune system. For type 1 diabetes, we can selectively suppress the immune system through the use of vitamin D3.

In the case of this last approach Professor Roep’s team has tested it with excellent results; the general immune system was preserved but the immune component responsible for attacking the pancreas was suppressed.

Professor Roep also made the point that having a variety of treatments means they can be tailored to individuals, just as cancer drugs provide a set of tools in the toolkit of the oncologist with patients responding to different ones.

Conclusions

While I enjoyed the entirety of ISPAD 2022, this was, for me, the stand out presentation. Challenging the type 1 origin story, presenting a model which is consistent with a broad range of observations, and pioneering resulting therapies is, for me, what these conferences are all about and I look forward to seeing the progress of Professor Roep’s work at conferences in the future.

LADAs Can Loop Too!

A little over three months ago I started looping with AndroidAPS and I could not be happier; my Time in Range is 90% and my HbA1c has gone from 6.8% and climbing, down to 6.0%. In case other LADAs find themselves in a similar situation to me, with their pancreas slowly failing, and wondering if looping is for them, I thought I would document the journey and confirm it is completely safe. As usual there is the tl;dr at the end for those who want to jump ahead the last page in the book.

September 2021-June 2022: Moving to Insulin

From diagnosis back in 2017 until September 2021, my HbA1c had always been less than 6.0%. Then in September my bloods came back with 6.1%. Considering that it could be due to the error margin I waited until my next appointment six months later to see the results. Unfortunately, my HbA1c in March was 6.6%. It seemed my pancreas was finally on the way out. Given I had set the line as not going over an HbA1c of 7.0% if I could avoid it, it was time to move to insulin. Discussing my glucose data with my endo we agreed that night-time was the area for immediate improvement and I moved to injecting long acting insulin before bed.

Three months on my HbA1c was 6.8% so night-time insulin was not enough an it was time to become fully insulin dependent., which I did starting with Multiple Daily Injections (MDI).

June 2022-July 2022: Injecting is not for Everyone

It turns out MDI was not for me or, at least, it was a skill which I was struggling to master. Using a flash glucose monitor, it was clear my injecting was doing little to stop mealtime spikes and Sugar Surfing with small boluses along the way was very inconvenient when using a pen injector, what with the changing of needle every time. I now understand why some people with diabetes do not swap out their needles with every injection, as is recommended. While I was intending to finally loop, even if I just moved to a pump I could more easily handle the spikes through mini-boluses at the press of a button and tracking my insulin on board should also be simpler.

At the same time, Insulet began offering half-price deals on their Omnipod wearable pumps (in Australia, Omnipod is completely unsubsidized via the public system or through private health insurance so this was compelling). I knew Omnipod was loop compatible so I jumped on board.

Concerns with Looping

Going from needles to a pump can be a big change given it comes with a loss of direct control; a machine is now putting insulin in the body, not your hand. This means there also has to be complete trust in the device not to fail. While national bodies such as the FDA in the US, and the TGA here in Australia set strong standards to ensure safety, as a LADA I have an advantage because I am still producing some insulin (in fact, my fasting c-peptide was tested in July and still sits in the normal range). A complete pump failure, for example, is unlikely to result in DKA and a trip to the hospital for me.

The looping algorithm also needs to be trusted. In the case of AndroidAPS, the documentation clearly sets out the algorithms used. The original algorithm was oref0, followed later by oref1. They are explained in detail here. Anyone concerned about the safety of OpenAPS and its derivatives, such as AndroidAPS, should read this. As a novice pumper, even if I did not quite understand everything in the oref0 and oref1 documentation, it was crystal clear the algorithms had been designed with safety as the first priority. Combined with the multiple studies on the efficacy and safety of the OpenAPS system, I was confident, as long as I took things slowly, everything was going to be fine.

In fact, AndroidAPS forces you to take things slowly through the Objectives in the app. You are literally unable to activate features such as looping until you have completed the designated tasks and shown a level of proficiency with the application.

It should also be noted that, unlike some commercial systems, OpenAPS/AndroidAPS has no machine learning/AI component. The reason I mention this is AI systems effectively generate their own algorithm without transparency so it is impossible to know precisely why an AI system performs a specific action.

The final concern I had, unique to LADAs, was the fact that I am still producing some insulin. The OpenAPS system was, arguably, built for classic Type 1s as an artificial pancreas, not as a supplementary pancreas. Speaking with one of the great minds behind OpenAPS, Dana Lewis, via Twitter, I raised this concern. Her response was:

“It doesn’t assume anything about insulin production. It takes your input settings (basal rates, ISF, carb ratio, etc) and uses that to assess whether you appear to need more or less insulin based on BG, trend etc.”

Considering the Issue of Endogenous Insulin Production in the Context of Looping

Let us look at the settings for a typical pump which relate to insulin delivery:

  • Basal Rate (how much insulin to trickle into the body to keep the liver from flooding the blood with glucose, fatty acids, and ketones)
  • Insulin Sensitivity Factor (how strongly the body responds to insulin for lowering blood glucose levels)
  • Insulin/Carbohydrate Ratio (how much insulin is needed to offset a specific amount of carbohydrate)

Let us also assume a hypothetical situation similar to my own where the body can produce enough insulin to cover the basal requirements for most of the day with perhaps a little extra insulin available, if needed.

The basal rate will simply be the additional insulin needed throughout the day. For my hypothetical this means much less (if any) in the day and more at night. No problems here.

Similarly, the insulin sensitivity factor should be largely unaffected by endogenous production when blood levels are stable. If I pump in a unit of insulin, it should have the same effect on my blood glucose level regardless of what my pancreas is doing.

The final parameter though, the Insulin/Carbohydrate Ratio, is affected by endogenous production. We can see this in the situation where the person eats a low carb snack. If the snack is eaten during the day, the pancreas can likely cover the snack without the need for pump insulin but, if I tell the pump what I have eaten, no matter how small, it will recommend a bolus. I cannot set a daytime ratio of infinity i.e. no insulin required, because if I eat a meal which has too many carbs for my pancreas to handle I will need insulin. What is missing is an offset value. So rather than using the formula:

Insulin required = (Carbohydrates consumed) / (Insulin/Carb Ratio)

it might be

Insulin required = (Carbohydrates consumed) / (Insulin/Carb Ratio) – maximum amount of insulin the pancreas can deliver above the basal rate

Let us consider an example to demonstrate what I am saying. If I eat an apple (10g of net carbohydrates) and my ratio is 5 then, according to my pump’s bolus wizard I will need 2 units of insulin (10/5) but, if my body can produce that additional 2 units, the pump does not need to do anything. However, if I eat a meal with 60g of net carbohydrate, the requirement will be for 12 units of which the pump will need to give me 10 on top of the 2 my body can produce.

The formula goes from I = C / 5 to I = -2 + C / 5.

How This is Resolved in Looping

So is there a flaw in oref0 or oref1 which puts LADAs in harms way? It has taken me this blog article to realise it but the answer is “no” and Dana is completely right. While blindly declaring carbs to a pump and bolusing will be inaccurate for low carb snacks, this is not how OpenAPS and AndroidAPS work. The very short version is they only give you insulin when the carbohydrates are seen in the blood, not when you declare them. Therefore, in the case of the apple, the loop will do nothing because the blood glucose levels do not change because the pancreas does its job whereas it will kick in the extra 10 units for the meal because there is a reaction in the blood due to the pancreas not being able to keep up.

Based on this difference between blind bolusing and reactive looping, it could be argued that OpenAPS looping is actually safer than manual management because there is a hypo risk in blind bolusing when eating low carb snacks which looping removes.

tl;dr

If you are a LADA considering looping, I recommend it without hesitation. In the case of AndroidAPS, the system guides you at a pace which allows you to understand what is happening as you go and, even though you may still be producing some amount of insulin, the looping system will accommodate this unlike the bolus wizard calculators of most insulin pumps on the market. The OpenAPS system is transparent in its approach and well worth considering if you have the means.

The Myth of Carbohydrate Counting

The myth is simply this:

“If you can accurately count the grams of carbohydrates in your meals, you can control your blood glucose levels”

It is a nice idea and one that many hold on to, including health care professionals. When I needed to start taking insulin for meals, the parting words of my endocrinologist were “You know how to carb count, right?” I have heard tales of parents, caring for their type 1 child, taking a weighing scale wherever they go, weighing food to the gram to calculate the total carbohydrates.

An obsession with carbohydrates, while understandable, can set up an unhealthy relationship with food. I have spoken before on the risk of mental health issues, such as  orthorexia, which comes from unnecessarily strict diets.

The myth reflects a reality in diabetes that very little is straightforward and simple with this disease.

The Carb Counting Process

If we take the process of counting carbohydrates and then calculating how much insulin we need, it goes something like this:

  • We get served a meal we intend to eat
  • Based on the contents, and nutrition guides, we determine how many grams of carbohydrates there are in the meal
  • Using a IC ratio (the number of grams of carbohydrate needed to offset a specific number of units of insulin) we administer the right number of units of insulin to counter the carbohydrates.
  • Our blood glucose levels remain perfectly steady and never, ever, go too high or too low

Sadly, many people who follow this process do not achieve the last point. Here are some reasons why.

Problem 1: Many, Many Factors Affect Blood Glucose Levels

There is a good reason why a meal on one day can have a completely different effect on blood glucose levels than on another day. Diatribe identify 42 factors which affect blood glucose levels, the vast majority of which are independent of meals and their composition.

Problem 2: Not All Carbohydrates Are Created Equal, Not All Meals Are Created Equal

15 grams of sucrose will hit the bloodstream faster than the carbs in a slice of white bread (also, approximately 15 grams). Eating a slice of white bread with butter will hit the bloodstream differently than eating it without butter (even though the carb count is practically the same).

The glycaemic index tries to quantify “carbohydrate speed” but this only measures single items of food e.g. an apple but not mixed meals e.g. an apple with cheese. Combine this with pre-bolusing (depending on the insulin, we may need to administer the insulin well before the arrival of the food) trying to match the peak activity of the insulin with the emptying of the carbs into the blood and we can see there is a fair amount of art to the science.

Problem 3: Food Labels Are Not Perfectly Accurate

Putting aside the fact that some food items do not even carry nutrition guides e.g. beer bottles, fresh fruit, restaurant meals etc. even the items which do are not bulletproof.

NIST suggest the error margin for carbohydrates on nutrition labels is 2-5%. So that slice of bread, taking 15g as our “middle value” has between 14g and 16g of carbohydrates in it. The larger the number of carbs, the larger this range. So do we bolus for 14, 15, or 16g of carbohydrate? Do we need to measure to the gram when the labels are this inaccurate? Then there is the question of fibre…

Problem 4: Total Carbs vs Net Carbs

Here is a nutritional label for a popular brand of white bread in Australia (perhaps it is a personal bias but I find Australian food labels much easier to read than, say, US ones)

For two slices of bread (one serving), we have:

  • 31.1g of carbohydrate of which 2.2g are sugars
  • 5.2g of dietary fibre

On US food labels these two values are combined to form “Total Carbohydrates” so, in the US, two slices of this bread would have 36.3g of Total Carbohydrates as opposed to the 31.1g of “Net Carbs” we see here.

So which do we use for two slices of bread? 36.3g (error margin plus or minus 1.8g) or 31.1g (error margin plus or minus 1.5g). For me the answer is clear. Dietary fibre, while chemically a carbohydrate, cannot be broken down in the gut into glucose and passes through undigested. So bolusing for it makes no sense and it is Net Carbs we need to embrace. There is also the issue of sugar alcohols but let us assume, for simplicity, our meals do not contain significant amounts of these.

Problem 5: Calculating the IC Ratio is Problematic

To accurately work out the IC ratio, the only way I can think of to do this with any level of precision is to

  • Work out the carbohydrate sensitivity (how many grams of carbohydrate are needed to change blood glucose by a fixed amount)
  • Work out the insulin sensitivity (how many units of insulin needed to change blood glucose by a fixed amount)
  • Assuming it is the same fixed amount, divide the grams by the Units. So, for example, if I know 16g of glucose tablets raise my blood sugar by 1mmol/L (18 mg/dL) and I know it takes 2 units of insulin to lower my blood by the same amount, my IC ratio is 16/2 = 8.

We know we have an error margin of 2-5% with the carbohydrate amount. So what about the other factors?

Assuming we take the perfect reading (clean hands, ideal temperature etc.), glucometers are considered accurate if 99% of readings are within 15% of the lab result value.

Insulin pump delivery is accurate to within 5% and it is probably fair to assume injection by pen or syringe has a similar level of accuracy.

With all these error ranges, we can calculate how accurate a calculated IC ratio really is.

For our carbohydrate sensitivity we have 16g (error margin of about 0.5g) raising our blood glucose by 1mmol/L (error margin 0.15mmol/L). So the actual value lies somewhere between 15.5/1.15 = 13.5 and 16.5/0.85 = 19.5 (some rounding applied to keep numbers friendly).

For our insulin sensitivity we have 2 units of insulin (error margin 0.1 Units) lowering our blood glucose by 1mmol/L (error margin 0.15mmol/L). In this case the range for our insulin sensitivity is between 1.7 and 2.5.

Combining these, our IC ratio falls between 5.5 and 11.5. That is quite the range and means the amount of insulin required to cover a fixed amount of carbohydrate could literally be double the value we think it is and there is no way to know what is correct because of the inherent uncertainty in the measurements.

So Where To From Here?

Clearly, we need to keep using insulin so what do we do? The first step is to embrace the uncertainty and to accept, for all of the reasons above, sometimes there are going to be bad days where blood sugars misbehave.

We could go ultra-low carb but, for me, this is simply not practical, nor desirable. I enjoy eating at restaurants with family and friends even when no nutritional tables are available nor ultra-low carb options. I travel for work and have meals as part of that where keeping to, say, 32g of carb per day is almost impossible.

For someone without a Continuous Glucose Monitor (CGM), the best they can do is make the best guess for their IC ratio and periodically finger prick to see how it went. Courses like DAFNE can help with guessing the right amount of insulin to use.

For the most part, I stick to “lowish” carbohydrates i.e. I look for lower carb options when out and about but manage the spikes and understand the occasional high will NOT do me damage, it is simply part of having type 1 diabetes.

Because I do use a CGM, for the highs, I have a two-pronged attack. Firstly, I am running a loop (Android APS). This suspends glucose delivery when I am low and constantly adjusts the rate of insulin to try and keep me at my target glucose level (currently 6.0 mmol/L = 108 mg/dL). Android APS is very clever at automatically detecting meals so I generally do not declare carbohydrates when eating. This is not the recommended approach with Android APS but, so far, it seems to be working ok. This being said, insulins cannot always keep up with food so I also “Sugar Surf” with mini-bolusing to assist the loop. With this approach it is important to be mindful of the “insulin on board” levels as we do not want to combat the high only to induce a severe low through insulin stacking. This approach would be very dangerous without knowing the levels of insulin on board which, for me, is provided by Android APS.

For finding a workable value for the IC ratio which, those of us who use insulin need to do, it is a case of trial and error. Nightscout, my open source blood glucose tracker on the web, has an “AutoTune” feature where it can analyse your blood glucose results for a period of time and provide a “best guess” for values such as the IC ratio. Similarly, Android APS has graphs for sensitivity, insulin on board levels and departures from the expected insulin/carbohydrate behaviour to inform adjustments to values. Also, setting different IC values across the day, and periodically reviewing them to make sure the value I am using is still useful, keeps me from having too many highs and lows.

Conclusions

Carb counting is not bulletproof. While a useful tool to have in the toolkit, it is not the only one available, nor should it be seen as the only one that matters. There is an inherent level of inaccuracy in carb counting and this means, without other interventions, blood glucose will fluctuate and occasionally go where we do not want it to.

Other tools available to us are eating low carb, if practical, exercise such as a walk post-meal can help, and insulin intervention delivered manually using techniques such as Sugar Surfing, or automatically via a loop can also assist.

Find the tools that work for you and understand nothing is perfect, including blood glucose levels and accept that while diabetes cannot be perfectly controlled it can, in the long run, be very effectively managed.

Finding My Basal Levels With A CGM (and My Pump Settings)

A couple of months ago I talked about how I was working out my overnight basal rate to keep my blood sugars in check. How things have progressed!

Since then I have seen my endocrinologist and my HbA1c continues to rise (now at 6.8%). The overnight highs are now in check so it seems my mealtime spikes are now the problem, thus I have moved to both basal and bolus insulin.

Another development has been the subsidy of CGM (Dexcom) and Flash GM (Libre) for all people with Type 1 Diabetes in Australia. This means I now have access to a CGM at a heavily discounted price (a little over A$30 per month).

Finally, Insulet, the makers of the Omnipod insulin pump had a special deal to get a month’s supply of wearable pumps for A$30 instead of the usual A$400 or so.

Along with my mobile phone, this means I have everything I need to set up an Android APS loop i.e. I have the CGM and pump to talk to each other, rather than have me inject insulin multiple times per day. To this end I have been setting up Android APS on my phone and working my way through the mini-tutorials/objectives in the Android APS app. In literally three weeks I have gone from injecting my first meal bolus to having a Low Glucose suspend loop in place, but I digress…

Pump Values

A key part of setting up Android APS (and insulin pumps in general) is determining the values for your profile i.e. your basal rate, how you respond to carbohydrates, insulin, etc. In the case of Android APS, your profile needs:

  • DIA: Duration of Insulin Action – This is a measure of how long insulin hangs around and acts on your glucose levels. It is measured in hours
  • IC: Carbohydrate to Insulin ratio – This is a measure of how much insulin is needed to counteract a specific amount of carbohydrate, expressed as a ratio. The fraction is Carbohydrates (g) / Insulin (U). Generally in science such a ratio would be called a “CI ratio” but, for some reason, history has labelled this one IC
  • ISF: Insulin Sensitivity Factor – This is a measure of your blood glucose level’s reaction to insulin, measured in U/(mmol/L, or whichever BGL units you prefer)
  • BAS: Basal Rate – How much rapid acting insulin do you need to deliver to your body to keep your liver in check, measured in U/hour
  • TARG: Target Glucose Level – What is your ideal default blood glucose level

All of these values are unique to the individual so how do we work them out?

Basal Rate (BAS)

For me, a good starting point was a direct conversion of my long acting insulin daily total to an hourly rate. So, overnight I was injecting 15U of Levemir and none in the day. Given my day rate was unknown, I used the same rate (pumps generally need a non-zero value) knowing this was likely a little too much and would need to be reduced. This was ok though because I now had a CGM on my arm (a Libre 2 which had been ‘encouraged’ to act as a CGM) and, if I was to go low, it was during waking hours and could be easily managed.

So 30U for the entire day divided by 24 hours is 1.25U/hr. This was my default rate.

To test whether the rate was accurate I split the day into three periods: 09:00-17:00, 17:00-01:00, and 01:00-09:00. Why these particular three periods? Because Android APS forces you to start the ranges at 00:00 and Novorapid (the insulin I am using in the pump) takes about an hour to get going. Also, they aligned nicely to human activity (working hours, evening hours, sleeping hours).

It was then a case of fasting and doing no boluses for a period (and trying not to do anything else to throw off my levels) and see if the line went up or down (allowing for a 10% margin of error on the reading).

As we can see above, while the rate stayed the same overnight, I did need to reduce it during the day as I kept creeping down over the eight hour periods. Generally I shifted the value in quarter unit increments but this will vary from person to person.

Your Basal Insulin Should Give You a Flat BGL!

I should make a point here because there seems to be a lot of confusion on it when I visit forums. If you do not eat, in the absence of other influences, your basal rate of insulin should give you a flat BGL line. If it is going up over time or going down it is not set correctly. Time and time again I hear stories of people missing a meal and going low (no pre-bolus) with an idea that this is the expected behaviour; that food is needed to ‘prop-up’ blood sugar levels.

If you regularly go low when missing a meal, and you have not pre-bolused, your basal rate is not set correctly. Without your basal rate set correctly, managing everything else becomes many times harder.

Here is an example I tweeted to illustrate the point.

This screenshot is in ‘Open Loop’ so Android APS is doing very little. Declarations of carbohydrate and insulin can be seen on the curve. Ignoring the spikes we can also see that the baseline is trailing downwards over the day, starting at 6mmol/L (108mg/dL) in the morning and hitting 4mmol/L (72mg/dL) just before dinner around 8pm.

So, at dinner, I have the conflict of combating my basal rate with carbs but also pre-bolusing for dinner. The result was the tripling of my blood glucose from 4mmol/L to 12mmol/L and a resulting rollercoaster. Not ideal. Doing eight hour fasts through the day, on different days, will give you a good idea on what your basal rate should be.

Insulin Sensitivity Factor (ISF)

The ideal time for me to test this was right at the end of the fast because it minimised confounding factors. The risk, of course, is that if you have been fasting, and your basal rate was too high, you may already be low and more insulin will give you a hypo. So you might want to do some tests when you are higher first to get a rough idea of your ISF value and then fine tune it after a fast with an amount of insulin which may send you lower but not dangerously so. So, for example, if you do some testing and conclude a unit of insulin lowers your blood glucose by 1-2 mmol/L, and you do not want to go below 4mmol/L, if, at the end of the fast your BGL is 6mmol/L then you could bolus 1U to see the reaction and be reasonably confident you will not go low.

For me, for the most part, this worked out fine. Only on one occasion did I go lower than expected and, in this case, I ended the test early and took a glucose tablet to halt the fall.

Carbohydrate to Insulin Ratio (IC)

It is really hard to measure this directly as it involves two inputs: Insulin and Carbohydrate, so I did not do it. Instead I measured how my BGL changed for a fixed amount of glucose e.g. a 4g glucose tablet, again generally at the end of a fasting period.

Measuring my Carbohydrate Sensitivity Factor (CSF) meant I could infer the IC because for a fixed change in BGL, we can use IC=CSF/ISF. For example, let us say my ISF is 2U/(mmol/L) i.e. 2U of insulin lowers my BGL by 1mmol/L, and I know 4g of carbohydrate raises my BGL by 1mmol/L. From this I can infer that my IC is 4/2 = 2 g/U.

A word of warning with this though, as it is the ratio of two variables, getting them both slightly wrong can have big consequences. So, for example, let us say my true ISF is 1U/(mmol/L) instead of 2 and my CGM was a little noisy making it hard to tell the difference, this has the effect of doubling my IC value (4/1 = 4). The solution is measure often and be conservative when changing values.

Target BGL (TARG)

This value is completely up to you but, my suggestion would be to keep it artificially high until the other factors are reasonably stable so you have ‘wiggle room’. I have started at 6mmol/L, will see how things go and probably bring it down as my confidence with the pump (and Android APS) grows. Some commercial looping systems put this closer to 7mmol/L (126mg/dL) and still get great results.

Duration of Insulin Action (DIA)

This is very hard to measure because insulins generally have a long tail and while, for one bolus this is not a big deal, with multiple there is the risk of insulin stacking and having a hypo. The complications involved in accurately measuring this are covered quite well here. My approach at this point is to try and tune the other factors as best as I can and then adjust this to see the effect. Based on advice from a veteran Android APS looper, I have set mine to 9 hours for now.

Shifting the Time Ranges of the Other Values

I mentioned above the ranges I set for the Basal Rates, but what do we do for the IC and ISF values we are measuring at the end of a Basal Rate period?

In my case I set new ranges where the end points of the fasting periods are in the middle of the ranges. Here, for example, are the ranges for my ISF values.

I may split these periods up in the future but this is how they are set now i.e. 04:00-12:00, 12:00-20:00, and 20:00-4:00.

Conclusions/What I Have Learned

While I could get an idea of my basal rate through incremental adjustments and finger-pricking, using a CGM and seeing how my BGL drifted over time made it much easier. It also allowed me to get an idea of my other pump settings which would be much harder with finger-pricking alone.

This is yet another reason why CGM technologies are so important for people with diabetes who are insulin dependent; it allows the person to see how they are tracking at a given moment in time but also over time which informs their overall management.

I also like this approach because it allows for on-going adjustment and the fasting periods can be set to suit my life, rather than the other way around; if I am working from home one day and no one else is around it is easy for me to skip lunch and test my basal, similarly for dinner.

If you have other techniques for working out your basal rates, feel free to add them to the comments and, remember, if your default blood glucose levels are not flat, fix your basal rates!

My Poster for ATTD 2022

Being part of the dedoc voices (https://dedoc.org/voices); a group of online advocates for people with diabetes who attend diabetes conferences and pass on what they learn to their respective communities gave me the opportunity recently to attend ATTD 2022, one of the largest and most prestigious conferences in the diabetes research calendar.

I had attended virtually last year and this had the unintended consequence of being added to the conference mailing list. While most emails advertised presentation by conference sponsors, about a year after attending, I received a “call for papers for ATTD 2022”. While not an academic in the field, I wondered if the subject matter of one of my blogs would make for appropriate content at the conference. So I submitted my blogs (here and here) on merging the reconciliation reports for Type 1 and LADA. To my shock and delight, it was accepted as a poster for the event.

Once dedoc discovered my submission had been accepted they also offered to fly me to the conference in Barcelona, Spain to promote my poster at the conference as I was the first dedoc voice who had a poster accepted for the event. Not only would I be participating in the conference, but I would also be there in person, rubbing shoulders with the greatest minds in diabetes research. It was very exciting. I set about putting my poster together which was simpler than I thought. I literally used Microsoft Visio to create the flow diagrams of my poster and Microsoft PowerPoint for the poster itself. It could not have been easier. With the generous help of others in the dedoc community with experience at submitting and reviewing academic medical posters, I put together something worthy of the conference.

The idea behind the poster was simple. In 2020, an international expert panel released a consensus report for the diagnosis and treatment of LADA (Latent Autoimmune Diabetes in Adults), also known as Type 1.5. A year later, the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) released a consensus report for the diagnosis and treatment of Type 1 diabetes. Given LADA is often considered a sub-type of Type 1 it is reasonable to expect the two reports to have consensus with each other, but they did not.

The poster sought to reconcile the two reports and, in doing so provided a flowchart for the diagnosis of diabetes across multiple types of diabetes.

Starting with someone showing classic symptoms of uncontrolled diabetes (thirsty, rapid weight loss, tired, frequent urination) the patient is first testing for auto-antibodies. A positive test immediately confirming Type 1/LADA. If negative, the age of the patient is considered. If they are less than 35 years old, and show signs of monogenic diabetes (MODY) such as a parent with diabetes and a relatively low HbA1c, combined with a medium to high c-peptide level, then genetic testing should be used to confirm or rule out monogenic diabetes. Next, we consider if there are typical Type 2 features such as an increased Body Mass Index. If so, we consider it as ‘provisional’ Type 2 and monitor the c-peptide levels every 6 months. If the c-peptide levels remain above 600pmol/L we consider them Type 2. If the levels drop below 300pmol/L, or there are not features of Type 2 diabetes, we assume it is Type 1/LADA without the presence of auto-antibodies.

For the first time, we have a diagnosis flow diagram for diabetes starting with a patient with symptoms, but an unspecified type, and we move through a series of tests to arrive at a diagnosis of MODY, Type 1/LADA, or Type 2 diabetes. For patients where it is still unclear whether it is Type 1/LADA without auto-antibodies or Type 2 diabetes, we have a clear cadence of checks until the right diagnosis is revealed.

From there, the reconciliation of the consensus reports led to a second flowchart for the treatment of Type 1/LADA and Type 2 diabetes. For Type 2 diabetes, the treatment is as specified in the consensus report for the management of Type 2 (also released in 2020). For Type 1/LADA, the results of the c-peptide 6-monthly checks inform the treatment. If the c-peptide levels are greater than 600pmol/L then the treatment follows the Type 2 protocol, with the recommended exclusion of sulfonylureas. If the c-peptide levels are 300-600pmol/L then the ‘LADA protocol’ is used which recommends the use of metformin with other adjunct therapies, depending on the presence of cardiovascular or chronic kidney disease. These adjunct therapies include DPP-4 inhibitors, GLP-1 receptor agonists and, if the HbA1c is sufficiently high, insulin (basal and/or prandial). If the c-peptide levels are less than 300pmol/L, the ‘Type 1 protocol’ is used which is effectively identical to the LADA protocol but specifies the immediate use of insulin (basal and/or prandial).

In the case of the second flow diagram, we have the basis for a well-defined protocol of treatment for Type 1, LADA, and Type 2 diabetes with treatment modifying as the disease progresses, in the case of LADA diabetes. Moreover, as new diabetes treatments are developed, they can be incorporated into the protocols, based on the evidence for their efficacy.

The importance of the poster is this is the first time we have a set of protocols that any health care provider can follow for the diagnosis and treatment of diabetes, backed by the international consensus of leading authorities. In my opinion these flow charts should be on the wall of the office of every health care professional who treats people with diabetes. While there is still the potential for misdiagnosis and mistreatment, by adopting a common standard, the flowcharts can be constantly improved to maximise the quality of care for people with diabetes.

Overall, I have really enjoyed the experience of putting the poster together and taking it to an international diabetes conference. The next steps are to collaborate with diabetes academics to write a peer reviewed paper on the subject. This will also provide the opportunity to update the recommendations with the latest conclusions from the literature in terms of medications but also in terms of devices such as continuous glucose monitors, pumps and looping technology.

What I have learned from the experience is this poster is proof that the voices of the diabetes community are important and can make a difference, not only in their own communities but on the international stage. We are worthy of participating in all arenas because no one knows diabetes as well as a person living with it. We are all experts of this disease in our own way and our experience and wisdom is important. If you have an idea or potential discovery which can help people with diabetes, do not be held back by doubt but pursue it. I promise you will not regret it.

My $20 Smart Insulin Pen

Being a Type 1 LADA my progression to full insulin dependence is quite slow (5 years since diagnosis and still only using insulin overnight). For now, I just need to inject long acting insulin after dinner to keep my night-time levels low and to counter Dawn Phenomenon. However, I often cannot remember if I have done it or not. Not wanting to give myself a double-dose, it would be great if I can digitally record when I give myself the injection and when.

Smart Insulin Pens

Smart Insulin Pens exist but are not cheap. The InPen retails for around US$550. The InPen does a lot more than just record injections but most of what it does I do not need so I wondered if I could create something simpler for less.

Flic Buttons

My day job is in low code software (Microsoft Power Platform to be precise) and, in my travels, I came across Flic buttons. These are Bluetooth-enabled buttons which can be used to trigger events via the Flic app on a paired mobile phone.

Attaching the Flic button to my insulin pen would give me the ‘smarts’ I needed. Using Blu Tack, I attached the Flic button just as you can see in the first picture in this article. Pairing was a case of installing the Flic app on my phone, running the app, and long pressing the Flic button. The app “heard” the button and did the rest.

Configuring the Actions

You can trigger off of:

  • Single Press
  • Double Press
  • Long Press

I tried to use the Single Press as a counter for the dosing, and the Long Press for the actual injection.

Count Presses is a standard function under the Fun category. Text Message is under the Communication category.

All you need to do for the Count is specify the name of the thing you are counting i.e. Insulin Units.

For the text message, you specify the phone number of interest and the message to be sent.

The Results

Counting the presses did not work out so well. While the presses accumulated and the total showed on my phone, there was no way to capture the number of presses at one time. The counter, as far as I could tell, only showed at the time of the button being pressed and never reset. The text messaging worked a lot better though.

The message captured the time and the action description, which was perfect.

Conclusions and Next Steps

For around $20 I have a smart insulin button which took me about 15 minutes to set up. While capturing the dosage would be ideal, given it is an overnight injection where the dosage is constant, this is not the end of the world. It is something I will continue to play with though to see if I can find a smart solution. Perhaps triggering a Power Platform Flow (or Zapier or IFTTT) to capture the button presses individually in a spreadsheet and then grouping the presses based on the timestamp will be the way to go.

If you are interested in setting up something similar, head on over to flic.io and pick up some of these handy buttons.

Finding My Overnight Basal Insulin Level

A little over a month ago I wrote how I was starting long-acting insulin at night and beginning the journey of finding the right level.

The good news is I have started to achieve gluco-normal levels in the morning and I am so excited I thought I would write about the path to get there.

The Background

A couple of years ago I did a literature review to work out at what blood glucose levels damage is being done to my body. The conclusions out of that were:

  • There is NO evidence that occasionally going over 140mg/dL (7.8 mmol/L) does damage. None, zero, zilch. So stop beating yourself up over a “bad day”. The damage to your mental health is not worth it. Win the war and do not focus on the odd battle that goes astray.
  • Keeping your HbA1c below 7.0% is good and, if you are at low risk of hypo, below 6.4% is better. Arguably, the lower you can go without exposure to serious hypos is a good thing
  • A fasting blood glucose below 120mg/dL (6.7mmol/L) is a good thing although the best predictor is HbA1c

My last blood results had an HbA1c of 6.6% and a fasting glucose of 7.2mmol/L so things had to change. This is where Levemir came in.

The Choice Of Treatment

I could shortcut to simply using a pump and continuous glucose monitor (CGM) which, in an ideal world, talk to each other to manage my blood glucose levels but, as I still do not require mealtime insulin, and that is a lot of equipment to manage (and pay for given CGMs are not yet subsidized for most people with Type 1 diabetes in Australia), I opted for a simpler solution of taking a long-acting insulin at night.

The insulin suggested by my endo was Levemir. While there are 24-hour insulins available (and weekly ones coming soon), the problem was overnight highs (confirmed by wearing a CGM a couple of weeks up to my endo appointment). We can see this in the excursions above 10 in the below plot which happen, almost exclusively, post dinner and continue until after midnight.

Levemir, with a roughly 12 hour action was a good choice.

Working Out The Dosage

The fact is there is no way to work out the right dosage without experimenting. Too little and blood sugars remain high, doing damage over time. Too much and you hypo which is dangerous and damaging. To quote a meme.

At my endo’s recommendation I started at 2 units and took measurements in the middle of the night and in the morning with a view of keeping the measurement in the middle of the night above 4.5mmol/L (80ish mg/dL) and between 4.5-5.5mmol/L (80-100mg/dL) in the morning. Each week I saw if I was in the goal range and, if not, incremented by 2 additional units.

This went on for a month but it was clear even 8 units was not doing much at all for my blood glucose levels. Clearly insulin resistance (which I knew I had) was working against me. My endo suggested jumping to 14 units and when this did not work, I went to 20 units.

Success!

This morning, for the first time in a long time, my morning blood glucose was in the set range.

Next Steps

Next is to fine tune the units to keep the average around 5.0mmol/L (90mg/dL) and minimise the variation. To measure this I will be looking at the 7-day average for the night and morning readings and the standard deviation. Both of these are readily calculable in Excel. My hope is adjusting the dosage and being reasonably strict on when I inject will keep these measures in check.

Conclusions/Things I Have Learned

  • Set your targets/goals early in your diagnosis: It is very easy to put off making a move to insulin, convincing yourself you will move more and eat less and it will all be better in 3-6 months time. I believe a better approach is have your past self set the goals for you when it is less likely emotion will influence the decision. It is also much harder arguing with your past self than it is with an endo who you can dismiss as not knowing your ‘lived experience’.
  • Tread carefully but purposefully: While it took a bit over a month to get near the right dosage, the approach was safe in the short term and set me up well for the long term
  • Continue to monitor, measure and improve: Getting my levels right now, and monitoring for change will set me up well for when I move to a pump and ensure I remain as healthy as possible. Injecting once a day and measuring twice a day seems like a small price to pay to minimise the risk of long term complications and short term hypos.

Insulin Cooling Battles: Breast Pads vs Breezy Packs

This is part of an on-going series where I compare different technologies available for keeping insulin cool so it does not spoil.

Previous battles were:

In this battle I compare Breezy Packs to breast pads.

Why Breast Pads?

It may seem like a curious choice but there is method to it. In “Frio vs Breezy Packs” I mentioned that Breezy Packs use Phase Change Materials (PCMs) to maintain the internal temperature. For a rundown of the physics on how they work, head over to that post.

While the specific material used in Breezy Packs is a trade secret, one candidate substance is octadecane whose melting point is around 28C (82.5F). While not listed on the box, on eBay the listing for the breast pads had octadecane as one of the main ingredients. For $20 it was worth a shot.

Sure enough, on touching the pad there was a cooling sensation so things were promising.

The Setup

For Breezy Packs, I used their smallest size and put one of my Ozempic pens inside with a digital temperature sensor embedded within it.

For the breast pads, I used a mesh pencil case I had picked up and layered the breast pads inside with another pen with a sensor between them.

In the image you only see the pads on one side but I did put eight on one side and eight on the other for the experiment.

A third sensor was used to track the oven temperature.

With the two containers on a rack on an oven tray (I did not want the tray to be in direct contact with the containers) I placed them in the oven and took the temperature around every five minutes until one of the containers went past 30C (86F).

Prior to entering the oven, the breast pads consistently measured a lower temperature than the Breezy Pack. I assume this was because of the higher area of contact between the pads and the insulin pen. However, things changed when the oven became involved.

The Results

While the breast pads initially showed a lower temperature, this soon changed. Both were pretty stable but, at 17:15, the temperature of the oven was continuing to fall and was heading towards 30C so I increased the dial by a small amount. The different response can be seen with the breast pads increasing temperature much faster than the Breezy Pack and eventually hitting 30C. In fact, over 40 minutes, the breast pad temperature went up by 7C (12F) compared to 2C (3F) for the Breezy Pack.

Conclusions

Breezy Packs wins again although I suspect if we used a similar volume/weight of breast pad PCM the result may have been different. This being said, the amount of breast pads needed to achieve this would be excessively expensive. As with previous experiments, the components were fully funded by myself without commercial sponsorship of any kind.

Grieving Change and Adopting Night-time Insulin

I had my six-monthly meeting with my Endocrinologist this week and it was clear the atmosphere was a little more formal than usual. Looking over my blood results he asked “Where do you want to start?”. Getting straight to the point I indicated the HbA1c number to which he replied “6.6%”.

This came as quite a shock as, for the five years since diagnosis, I had been 6.1% or less. The number had drifted up slightly in the last few results but this was quite the jump. “Well that’s it”, I concluded, “it is time to get onto the good stuff” which, in this case, meant insulin.

Set Your Goals and Limits Early

Back in November 2019 I had written a blog on what levels a person must maintain to limit damage and the risk of long term complications. My conclusion had been an HbA1c over 6.4% significantly increased the risk of complications and, once I reached this mark, it was time for insulin. That time had come.

I cannot recommend highly enough setting these kinds of personal limits early in your diabetes journey. Talk to your health team, review the literature as I did and create your own lines in the sand. When the time comes, they will serve you well because it is only natural to try and maintain the status quo when, in fact, change is necessary. Let your past self guide your present self so you can both look after your future self.

Grieving the Loss of Familiarity

I am a firm believer people go through grief when faced with significant changes in their life. The move to using insulin while not a big deal in itself, is such a change. While my other medications are to slow the progression of the disease and will no longer be needed in the future, the move to insulin is, in all likelihood, a permanent one.

The grief felt is, I believe, the grief of losing familiarity, of entering a new normal and the adjustments which come with it. In fact, the Kubler-Ross Grief Stages have been modified for chronic disease to cover this very concept.

In reviewing my HbA1c it was tempting to defend the result by considering the large error margin that is inherent in the HbA1c measurement (Denial) but, thanks to my regular reporting for my endo, I knew this result was consistent with the trend of my numbers heading upward and unlikely to be an outlier.

Without my line in the sand of 6.4% it would have also been tempting to give it another, say, six months, resisting my endo’s recommendation for insulin intervention and see where the numbers landed, doing my best to exercise more and eat less sugar in the interim (Pleading, Bargaining, and Desperation) but past Leon had prepared for this day allowing me to move past the second stage, reconciled by evidence over emotion.

There was a little Anger at myself for not doing more e.g. more exercise and less candy but, in reality, the immune system always wins. Five years of insulin independence was a remarkable achievement and it is mentally much healthier to focus on what was achieved than what was not.

There was also a little Depression that the inevitable had arrived along with small feelings of Loss of Self but, again, equipped with the knowledge that this was always going to happen helped me get past this. While there may still be some lingering feelings of this (while I write this it has been three days since I saw the endo), it is time to accept the next stage of my journey.

Acceptance and the Road Ahead

I had put on a sensor in the weeks leading up to the meeting with the endo so we had good information to guide us on where the high sugars were.

It is clear in the trace that from around 9pm at night my numbers go up and slowly drift down until morning. It is also clear that this drift sits around 7 mmol/L (126 mg/dL) and should probably be closer to 5 mmol/L. I remember when I was first diagnosed my blood sugars would sit in the 4s outside of meals but I have not been there for a very long time. This night-time elevation is where we decided to target the levels.

Long Acting Insulin

I spoke about the two main roles of insulin back in 2019. In short, if you are not eating, your levels should be reasonably flat and towards the bottom on the standard range e.g. 4.5-5.5 mmol/L (80-100 mg/dL). If the levels are not behaving like this, it is likely the body is struggling to keep the liver’s glucose release in check. For multiple daily injection, this is the role performed by long acting insulin.

As neither I nor my endo know exactly how much insulin is needed to flatten the overnight curve, we are starting conservatively: 2 units of Levemir taken at around 9pm and monitored once in the middle of the night (checking I am not too low), and once in the morning to see if I am between 4.5 and 5.5 mmol/L.

After one week I will see how it has gone and, if I am not getting down low enough, I will increase the dosage by another 2 units, monitor for another week, rinse and repeat.

There are a few long-acting insulins available but Levemir is useful for my specific purpose because it has peak activity for 12 or so hours which matches the period of time I need it for.

The New Normal

This is now my new normal; injecting once at night and for now, monitoring levels twice per day until I get the dosage right. My new goal is to reduce my HbA1c to a healthier level (sub-6% without hypos would be ideal) and if I can do this without the need for mealtime insulin, even better. Of course, if I continue to have an HbA1c above 6.4%, additional measures will be required. Again, the measures I have set for myself, informed by my own investigations and supported by discussions with my health care team, will guide me and allow me to keep a level head no matter what happens.