The Practical Diabetic Solution: The Modern Guide To Achieving Normal Blood Sugars (or Pretty Good Blood Sugars, You Decide)

This week I underwent an experiment to see what would happen if I combined a very low carbohydrate meal replacement, a commercial looping system, and snacking to cover hunger pangs. The results were better than I expected and, over the four days, I was seeing normal, non-diabetic blood sugars. Unlike other regimens, I did it with:

  • No exercise
  • No bolusing
  • No hypo treatments
  • No meal plans
  • With insulin resistance and a daily insulin requirement of over 70 units per day

You can see the details of the setup here but, in this post, I thought I would go through the results and, now I am on the other side, reiterate why I believe it is a superior approach to Dr. Bernstein’s.

Before and After

So, before the four days, I had:

  • Average Glucose of 7.2 mmol/L (130mg/dL) (over 14 days)
  • Average Glucose of 6.5 mmol/L (117mg/dL) (over 2 days)
  • Standard Deviation 1.9 mmol/L (34 mg/dL) (over 14 days)
  • Standard Deviation 2.3 mmol/L (42 mg/dL) (over 2 days)
  • Median 6.2 mmol/L (112 mg/dL) (over 24 hours)
  • Coefficient of Variation 35% of Mean (over 2 days)
  • Time in Tight Range (3.9 – 7.8 mmol/L aka 70 – 140 mg/dL): 65%
  • Highs: 7 Lows: 11 (over 2 days)
  • GMI of 6.1% (over 2 days)

Let us now look at the results at the end of each day (screenshots taken just after midnight each night)

Day 1
Day 2
Day 3
Day 4

For the totals above, as can be read with a keen eye, all graphs are for 24 hours. The range is the Time in Tight Range (TITR) (3.9 – 7.8 mmol/L aka 70 – 140 mg/dL).

Comparing we see every measure (except the Median, especially on Day 3) has significantly improved. Highlights include:

  • Halving the Standard Deviation and Coefficient of Variation.
  • Taking my TITR from the mid-60s to the high 90s
  • Eliminating my lows (although I suspect they were calibration errors from a new sensor) and significantly reducing my highs (these were real).

For completeness, my weight stayed about the same, and my daily insulin requirement stayed about the same (84-79 units) as well. This second result genuinely surprised me as I assumed the sudden drop in dietary carbohydrate would lead to a much lower insulin need. I assume the difference in carb was offset by the increased protein and further amplified by the increased consumption of animal fats, raising my insulin resistance.

Did You Really Achieve Normal Blood Sugars?

Let us consider a study of the blood sugars of non-diabetics I mentioned in another recent post.

Lots of numbers here, so let me translate the key points for the average participant:

  • They had a mean value of 99 +/- 7 mg/dL (5.5 +/- 0.4 mmol/L)
  • Their standard deviation was 17 +/- 3 mg/dL (0.9 +/- 0.2 mmol/L)
  • Coefficient of Variation was 17 +/- 3 %
  • TITR was 93-98 %
  • Time in Super Tight Range (TISTR) (70 – 120 mg/dL aka 3.9 – 6.7 mmol/L) was 82-92%
  • Time below range was about 1.3% of the time

I only measured TISTR once during the four days which looked like this:

Where I measured 92% TISTR, beating the non-diabetic value of 90% and hit every range on the non-diabetic normal blood ranges.

The only measure I did not consistently hit was Mean Glucose on days two and three due to my morning coffee throwing out my values. By day four I had adjusted the coffee not to spike me so I think it is fair to say that, with improved experience managing the snacks and setting my pump to a more aggressive target (it was set to 5.4 mmol/L aka 98 mg/dL for the experiment but can be set as low as 4.4 mmol/L aka 80 mg/dL), given I had zero lows during the four days, it would not be hard to consistently hit this range as well.

Why Do You Say It Is Superior To Dr. Bernstein’s Approach?

In terms of the results I expect it is possible to get similar results with Bernstein but where I see this approach having the edge is:

  • Food management is MUCH simpler: Aussielent takes care of the main meals and you simply choose snacks which you like and which work for you. Compare this to Bernstein where you have to craft meal plans (he literally wrote a nearly 300-page book just on this topic alone), have no snacking, have to consider “forbidden” and “allowed” foods; it is a lot more work
  • Insulin management is MUCH simpler: Getting the looping pump to do the heavy lifting means I literally go for hours a day, not thinking about diabetes and I never need to “sugar surf” my way down. For the above results I did not even declare carbs or bolus; the loop took care of it. In the case of Bernstein, from Dave Dikeman’s video which I mentioned in my preparation blog, we learn he treats lows with glucose 1-2 times a day and, if he goes above 110 mg/dL (6.1 mmol/L) he uses an intramuscular shot of rapid acting Novolog. This is not including any R-insulin injections he does to cover meals, plus injections for basal and dawn phenomenon management.
  • Hormone fluctuation management is MUCH simpler. A good example of this is dawn phenomenon. For someone who is looping, the pump manages it overnight with no human intervention required. Here, Dr. Bernstein admits he and most of his Type 1 patients go up overnight and his solution is getting up, every night around 4am and doing multiple injections of different insulins which, to me, is a recipe for disaster.

The fact is the most recent edition of Dr. Bernstein’s book was written over ten years ago and a LOT has changed since then. It make sense the innovations which have come over the last decade, such as looping systems, can help us manage diabetes better and remove some of the mental burden of managing the disease.

The other big advantage of the Practical Diabetic Solution is there are still plenty of levers to pull for even better results e.g. the inclusion of exercise, bolusing and declaring if required, flexibility in snack strictness to suit the individual, augmentation of pump delivery with needle delivery etc. whereas, with Bernstein, it is so strict, there is, in my opinion, little room to move or to be creative.

Will I Be Continuing The Practical Diabetic Solution?

My position has not changed. To explain my position, I will again quote Dr. Bernstein adherent, Dave Dikeman: “I want to be normal…Not normal in that I can eat a birthday cake with everybody else but normal in that I want to have the same blood sugars as everyone else”. I respect this position but I simply do not share it. I see no reason why I cannot have a small slice of cake at the occasional birthday party, estimate the bolus and have the loop soak up the rest and my Solution allows for that. My goal is to minimise maintenance and maintain blood sugars enough to minimise the risk of complications, helped by regular check-ups.

Similarly, if I go to a restaurant, I do not want to pull out a meal plan meal and eat it while my family orders; I want to share in the experience with my family and experience the food as the chef intended. Food is an integral part of human social interaction, it is even in our language; the word “companion” comes from “someone who you break bread with”, “mate” comes from “someone you share your food (meat) with”, and to nurture comes from the concept “to feed”. To shun this link is to shun who we are.

Where I am likely to embrace the Solution is at breakfast, lunch and while travelling. Morning is a rushed affair in our house so a quick meal shake which I do not need to think too hard about is perfect. As I mostly work from home, I usually eat lunch alone so, again, a shake which will not spike me and make me a zombie in the afternoon, which is perfect. Conversely, if I go into work and my colleagues go out for lunch I will join them and leave the shake in the locker. Dinner is around a dinner table and shared with the family. This is sacrosanct for us and the Solution will not be part of it.

For travelling, the Solution is perfect. At conferences or, for example, all day workshops, there is often limited eating options and the options provided are often carby. A meal replacement shake is easy to carry with me and removes the issue.

What About You?

For someone looking for some stability in their numbers and piece of mind, consider the Solution. As mentioned here, the latest clinical thinking is an HbA1c below 6.5% or a TITR of greater than 50% is sufficient to avoid the risk of long term complications. Even if you just replace breakfast, you will likely be gluco-normal through the night (thanks to the loop) and up to lunchtime, which is already more than half the day i.e. more than 50% TITR. Anything above and beyond this is a bonus.

For the person aiming for normal blood sugars, the plan, as I followed it, is worth considering and the barrier to entry and exit are quite low as it does not require exercise, food plans, and kitchen overhauls (other than waiting a few days for the Aussielent or equivalent to turn up). If, like me, family dinner is important, you can “snack” on the elements which will not spike you which they are eating, while drinking your meal replacement (which is what I did this week). I literally saw stunning results by the first day so try it and, if you do not see improvement, move on.

Roadtesting An Approach “Better Than Bernstein”: The Preparation

Let me start by making it clear I am quite the fan of Dr. Bernstein. I have his books and have watched all of the Diabetes University videos on YouTube. If you are new to diabetes and want a foundation on the disease and how it works, his videos are a great place to begin. Dr. Bernstein took responsibility for his disease and came up with a solution which worked really well for him. He then published his method and a lot of people have success with it. However, the last version of his book published was over 10 years ago. A lot has happened in regards to technology, medications, and food options in that time so I thought it was worth exploring how to improve on his work for my own personal benefit and that of the diabetes community.

What Is Dr. Bernstein’s Diabetes Solution?

I had a quick browse through my copies of “Dr. Bernstein’s Diabetes Solution” and “The Diabetes Diet” but could not find a good summary of his approach. Diabetes Daily give some good context on the man and the solution which may be worth a read. In short, Dr. Bernstein’s goal is for people with diabetes to have “normal” blood sugars i.e. blood sugar levels indistinguishable from non-diabetics. His approach involves:

  • Low Carbohydrate (less than 30g/day) and high protein/moderate fats
  • Three meals per day, no/limited snacking, with each meal having effectively the same macronutrient profile each day
  • His starting suggesting is a breakfast with 6g carbohydrate, lunch with 12g carbohydrate, and dinner with 12g carbohydrate
  • He advocates regular exercise which promotes muscle growth, weight loss, and improves insulin sensitivity
  • “Insulin Hacking” i.e. intramuscular injections using rapid insulin
  • He is generally against the use of technology in his book, preferring multiple daily injections although concedes Continuous Glucose Monitors (CGMs) may have their uses (“If I were living alone, I’d use a CGM to protect from nighttime hypoglycemic episodes” – Diabetes Solution, p357). For pumps, Bernstein lists a range of advantages and problems on pages 330-332. Quotes include:
    • “Corrective injections are elegantly simple” – Diabetes Solution, p331
    • “Pumps can be set to automatically increase the basal delivery rate shortly before arising in the morning, thereby circumventing problems associated with the dawn phenomenon” – Diabetes Solution, p331
    • “Insulin pumps cannot be used to give intramuscular injections for more rapid lowering of elevated blood sugars” – Diabetes Solution, p331
    • “Contrary to a common misconception, they do not measure what your blood sugar is and correct it automatically” – Diabetes Solution, p332
    • If you have the book, check them out. For me, many of the criticisms of pumps equally apply to multiple daily injections over a prolonged period but decide for yourself

To see Dr. Bernstein’s Diabetes Solution in action, Dave Dikeman is a great example. He has been living with type 1 diabetes since the age of nine (he is now around 18 years old) and has worked closely with Dr. Bernstein, (I believe assisting with his YouTube channel) for many years. He presented his approach to Low Carb Down Under about a year ago. It is a great summary of how the solution works and shows someone achieving great success with it.

What Results Can We Expect From Dr. Bernstein’s Diabetes Solution?

Fortunately, Dr. Bernstein published the results of people dedicated to his approach five years ago. Key results were:

  • A survey was conducted on members of the Facebook “Typeonegrit” group with 316 respondents, a group of “type 1’s and parents who follow Dr. Bernstein”
  • Average time following Dr. Bernstein’s Diabetes Solution was 2.2 ± 3.9 years
  • Mean daily carbohydrate intake was 36 ± 15 g
  • Average HbA1c was 5.67% ± 0.66%
  • 2% of respondents reported diabetes-related hospitalizations in the past year

My Current Approach And How It Compares

Using the Bernstein summary as a prompt, here is my current approach:

  • “Low-ish” carbohydrate: I do not count carbs but estimate I eat maybe 100-150g per day
  • I generally have a white coffee for breakfast, nothing regular for lunch (sometimes food, sometimes snacks, sometimes nothing), and dinner with the family which usually has no more than 50g per serving, but this is not a hard rule
  • Snacking happens when I want. It is small and I do not give it too much consideration
  • Little to no exercise
  • I use a commercial looping pump/cgm. No injections, no finger pricks
  • I do not declare any carbohydrates, do not bolus or boost; the loop takes care of it

In terms of the results I am getting, I have been looping for close to 12 months and my last HbA1c was 5.5%. Given I am not following Dr. Bernstein’s Diabetes Solution at all and getting superior results to the average participant in the Typeonegrit survey, over a shorter period of time, perhaps there is value in assessing a hybrid approach for even better results.

Simplifying Food

A big part of any summary of Dr. Bernstein’s Diabetes Solution involves the listing of forbidden and allowed foods. In the Diabetes Daily summary mentioned above, of the 5,700 words, 4,500 describe which foods can and cannot be eaten. That is over 3/4 of the description. In Dr. Bernstein’s Diabetes Solution, chapters 9, 10, 11, and 25 (roughly 120 pages out of 460 pages or a quarter of the book) cover food and its management. I think it is fair to say food management is a big part of Dr. Bernstein’s Diabetes Solution.

Two years after the last version of Dr. Bernstein’s Diabetes Solution came out, a company called Soylent appeared offering nutritionally complete meal replacements for time-poor people who do not like cooking. Other companies offer similar products, including Aussielent which also offer a low carbohydrate alternative (shown below).

A serving provides about a quarter of the body’s micro-nutrients. For macro-nutrients a serving provides:

  • 1700kJ (406 Cal)
  • 30.4g Protein
  • 26.9g Fat
  • 7.1g Carbohydrate (excluding fibre)
  • 5.4g Fibre

So, in theory, four servings a day will provide all the micro-nutrients the body needs. It passes the “less than 30g of carb per day” test of Bernstein and gives a total energy of 6,800kJ (1,624 Cal). The average adult requires between 8,700kJ – 10,500 kJ (2,000 – 2,500 Cal) per day to maintain a healthy weight (, so we have a deficit of at least 2,100 kJ (500 Cal). Also, the packet is clear in saying “Not to be used as a sole source of nutrition”. So, we can embrace the energy deficit and lose some weight or use it for snacking. As long as the snacks do not spike us we are good to go. There are plenty of foods which, as people with diabetes, we know we can eat without spiking. For me, I will be eating things like:

  • Home made protein balls (about 735 kJ/175 Cal each)
  • Cheese and crackers (516 kJ/125 Cal)
  • Water Chestnuts and Soy Sauce (about 190 kJ/45 Cal)

Drinks will be sugar free so it will be diet soft drinks, mineral water (soda water), sugar free cordial, and tea/coffee.

I also only have enough Aussielent for four days so this will be the length of the experiment.


There is no doubt exercise is good for anyone. I will not be changing my routine for the next four days though. Clearly, if there was a desire to make this a long-term venture, introducing exercise would be good. Keeping this as=is also removes it as a confounding variable in the results.

Measuring and Administering Insulin

I have no doubt the use of a CGM and a Pump, with looping, have been a big part of my success to date. The pump is watching my blood sugars every five minutes and making adjustments to move my levels towards my target (currently 5.4 mmol/L or 97 mg/dL). Unlike Dr. Bernstein’s Diabetes Solution, which relies on basal insulin (sometimes delivered in the middle of the night to counter dawn phenomenon), and injecting insulin into muscles, my loop has no reliance on me being awake, or “insulin hacking”.

Looping was not available when the last edition of Dr. Bernstein’s Diabetes Solution came out which is why he says “they do not measure what your blood sugar is and correct it automatically”. Today, they can, and are very, very effective at managing overnight and hormonal fluctuations.

How Will I Measure Success?

My plan is to document my baseline in this blog and then review afterwards and see what has changed.

Current weight: 112kgs (246 lbs)

Last Daily Insulin Amount: 84 Units

Diasend (

  • Average glucose: 7.2 mmol/L (130 mg/dL)
  • Standard Deviation 1.9 mmol/L (34 mg/dL)
  • Time in Tight Range (3.9 – 7.8 mmol/L aka 70 – 140 mg/dL): 65%

Sugarmate (

  • % in Range (daily TIR 3.9 – 10 mmol/L aka 70 – 180 mg/dL): 69%
  • Time Below Range: 7% / Time In Range: 67% / Time Above Range 26% (TIR)
  • Average 6.5 mmol/L (117 mg/dL)
  • Standard Deviation 2.3 mmol/L (41 mg/dL)
  • Median 6.2 mmol/L (112 mg/dL)
  • Coefficient of Variation 35% of mean
  • Highs: 7 Lows: 11
  • GMI: 6.1%

Tidepool (

1 week values

  • Time In Range (4.0 – 10.0 mmol/L aka 72 – 180 mg/dL): 88%
  • Time Above Range: 9.3%
  • Time Below Range: 3%
  • Average Glucose: 7.2%
  • Standard Deviation: 1.9 mmol/L aka 34 mg/dL


  • The lows are due to poor readings of the CGM on insertion, as confirmed by finger pricks (the only time I do them). For me, the G6 sensor reads low for the first few days after insertion
  • Variation between the reports is generally due to differing periods of review. For Diasend it was the last week of data, for Sugarmate it is written on the measure (some say 2 days even though I specified 3, I am not sure why this is the case), and Tidepool was one week.

Are You Planning To Continue With This Approach?

Only so much can be demonstrated over four days. My primary reason for doing this is to see if Aussielent meals are a viable option when I am travelling for work as I have less control over what I eat when at conferences or onsite with clients. Carrying some powder and olive oil while travelling is a relatively simple solution. However, if I can also develop some preliminary data combining looping technology and a very-low carbohydrate diet, this may be worth more analysis later either by me or other people curious to try different approaches.

I actually have no interest in pursuing a very-low carbohydrate regimen long term. The primary goal of Dr. Bernstein’s Diabetes Solution is normal blood sugars. My goal is to minimise maintenance as much as possible to reduce the risk of burnout i.e. a sustainable approach, and to minimise the risk of long term complications (which is not quite the same as normal blood sugars). What I mean by this is maintaining a sufficiently low HbA1c that clinical evidence suggests I am close to the same risk of long term complications as a non-diabetic and getting regular check-ups is enough for me; I do not need to obsess about every spike or deviation.

Also, I like going out to restaurants and eating meals as the chef intended; I enjoy eating in moderation, rather than fixating on forbidden and approved foods; I enjoy spending literally hours a day not thinking about diabetes management. I see no compelling reason to change any of this.

Where To From Here?

For the next four days, I will be following the “Improved Solution” and writing about it next weekend. I will also be getting blood work done towards the end of this week as I am seeing my endocrinologist soon. This will give me additional results which I will publish later.

ATTD 2023: What Is The Right Time In Range?

I had the privilege of being a Dedoc Voice in Berlin at ATTD 2023 this year. While there were many fascinating discussions (many of which I Tweeted about at PracticalDeeb) there was one in particular that really stood out and that was a frank and open discussion on the clinical relevance of Time in Range and whether it needs revising.

For those who want to cut to the chase, there is a tl;dr at the end.

What is Time In Range (TIR)?

Before launching into the presentations at ATTD, it is probably best to explain the term Time In Range. Thankfully, I have already written a piece explaining it, using a presentation from EASD 2020 by Professor Pratik Choudhary (who was my t-shirt hall of fame recipient for the conference).

In short, the default standard is the range 70-180 mg/dL (3.9-10 mmol/L) and the traditional target was to reside within this range for more than 70% of the time, as measured by a Continuous Glucose Monitor (CGM).

This presentation at ATTD 2023 put the target under the microscope to see if it needed revising.

Time in Tight Range: The New Standard?

Professor Thomas Danne introduced a concept of a Time in Tight Range (TITR) which reduces the range to 70-140 mg/dL (3.9-7.8 mmol/L). Why a new range? Because Professor Danne literally said “I don’t want to lie any longer”.

The suggestion was, to live a normal, healthy life, 70% TIR was not enough but to give truth to what needs to be achieved would discourage when encouragement was needed so a “soft target” was given instead. This admission will vindicate many online pundits who rail against TIR as insufficient to avoid complications. In essence, this has now been confirmed.

An advantage of considering TITR is spikes, which may remain within TIR but not TITR, can be identified and worked on, assuming managing levels within TIR has been achieved.

It is interesting to note that Professor Danne considered 70-140 as “normoglycemia” i.e. normal blood sugars and above 140 as “dysglycemia” (not normal blood sugars) and therefore concluded TITR can also be used as a range for early detection i.e. Stage 2 Type 1 Diabetes (when blood glucose levels are not normal but insulin is not yet being used). Professor Danne also cited a paper that concluded that time above the tight range predicted the progression to Stage 3 Type 1 Diabetes i.e. when insulin is required.

Professor Danne went further and stated he felt the latest ISPAD Time in Range guidelines do not go far enough, claiming the life expectancy of a child with type 1 diabetes will not be the same as a child without type 1 diabetes using these targets.

His preferred goal? An ambulatory glucose profile characterised as “Flat, Narrow, and In Range” (FNIR).

The Gritters can raise a glass of alcohol-free, non-fizzy coconut milk and celebrate that academia is beginning to align to their strict goals. So did Professor Danne go on to talk about all people with type 1 diabetes adopting an ultra-low carbohydrate diet, and eating a strict three meals a day? Well, no.

As alluded to earlier, his goal is to provide guidance to people with diabetes and their carers which is considered achievable and sustainable, even if this means historically softening the targets. Also, Professor Danne made it clear a qualitative daily target was insufficient but a SMART (Specific, Measurable, Achievable, Relevant, and Time-Bound) goal was also needed i.e. quantitative as well as qualitative. His solution? Automated Insulin Delivery i.e. Looping.

His evidence that AID leads to improved results? A comparison across countries of HbA1c pursued through various means compared to Time in Range pursued through AID. Even in the best performing country (Sweden) people with type 1 diabetes struggled to get an HbA1c below 7% (50 on the scale). However, all countries consistently achieved a TIR above 70% which is broadly equivalent to an HbA1c of 7% using AID.

But are we not considering TITR, not TIR? Alas reporting on TITR is still quite limited but Professor Danne is hopeful. On top of using AID, he also mentioned the results being achieved with SGLT2i drugs (which basically redirect glucose in the blood to the bladder, keeping blood glucose levels low).

The jury is still out on the use of SGLT2i’s in people with type 1 diabetes because of the increased risk of eDKA but Professor Danne is hopeful, the rise of continuous ketone sensors will address this. For someone like me who still has residual pancreatic function, the use of an SGLT2i is more compelling because the residual insulin means any form of DKA is extremely unlikely.

More evidence of the superiority of AID over other methods came from a Cambridge study which showed improved sustainable performance over two years.

Doctor Peter Adolfsson continued the story by presenting on the specifics of what those SMART goals should be.

First he talked at what normal blood sugars in children look like where the TITR is close to 90%

A more recent study with more accurate CGMs puts the number at 96% TITR

Doctor Adolfsson then moved the discussion to what target do we need to achieve, not to match people without diabetes, but to reduce the risk of complications to match the non-diabetic population and suggested an HbA1c of 6.5% was sufficient for this which corresponded to a TITR of 50%. This comes close to the conclusions I came to a while ago that an HbA1c under 7.0% is good but, if it can be achieved without severe hypo risk, an HbA1c of 6.4% is better.


Professor Danne acknowledged that, historically, advice to people with type 1 diabetes had been targets which still exposed them to long term complications because it was simply too hard and arduous for the client to achieve tighter targets i.e. the goal was harm minimisation rather than elimination. However, the advent of Automated Insulin Delivery (AID) / Looping has meant it is much easier to achieve superior results with minimal additional effort.

This has led to the consideration of the Tight Time in Range (TITR) which puts the goal for glucose levels to be between 70-140 mg/dL (3.9-7.8 mmol/L). This new range has the potential to be diagnostic of the stages of type 1 diabetes as well as provide improved guidance for glucose control.

In terms of the percentage of time to aim for in the new range, for truly normal blood sugars, the target is 96% of the time. However, there is no evidence that can be achieved through AID. The compromise target is to aim for a percentage which reduces the risk of complication to that similar to the non-diabetic population. Research suggests this lowers the target percentage to 50% TITR which corresponds to an HbA1c of 6.5%.

In other words, rather than pursue the goal of “normal blood sugars”, the goal is “free of long term complications”. What I personally like about this approach is TITR can be measured, at home, by anyone with a CGM (unlike HbA1c). Also, the individual can choose how strict they want to be in pursuing “normalcy” i.e. sit at 50% TITR and minimise the risk of complications or go harder to achieve the blood glucose levels of a person without diabetes. This latitude in the percentage allows flexibility in terms of the individual’s personal circumstances which, in turn, minimises the risk of burnout.

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.


The easiest way to access Autotune is via Mark Carrington’s online interface at

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.


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.


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.


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.


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.

My Poster for ATTD 2022

Being part of the dedoc 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.

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.


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.


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.