The Practical Diabetic’s Ten Levels of Medical “Facts”

Not all medical information is equal, even if it comes from a reliable source. To help me filter the wheat from the chaff, I created these ten levels ranging from Idle Speculation up to verified Medical Fact. Let us get into it and, as is often the case, we have the tl;dr at the end.

Level 1: Idle Speculation

The least reliable medical fact, this is conjecture with literally nothing to back it up. An example might be “I reckon lies cause head colds”.

Level 2: Secondary Source Anecdote

Something someone has heard from somewhere else. The evidence is a “friend of a friend” who had success with the approach. An example might be “Yoga cured my aunt’s diabetes”. Perhaps she was cured, perhaps her management improved. Perhaps she was pre-diabetic or, perhaps, it was gestational diabetes which went away after pregnancy.

Level 3: Primary Source Anecdote

It worked for the person telling you. A good example of this is my “Practical Diabetic Solution”. While, since publishing the article, many people have said things along the lines of “I have a similar approach which works for me”, the fact is, at the time of writing this article, the only person to try the Practical Diabetic Solution is me because I literally wrote about it seven days before writing this article.

Level 4: Multiple, Corroborating Anecdotes

Many people have tried a similar approach and claim to have success. The quotes you see on dodgy supplement sites or on the back of books fall under this category. While the quote may be genuine, sample selection is often biased (when was the last time you saw a bad review on the back of a book?).

Level 5: Observed Under Controlled Conditions

Social experiments often fall under this category; the rules are set and then let to play out to see what happens. The movie “Super Size Me” is a good example of this where the movie’s maker followed a set of rules for engaging with the fast food restaurant, McDonald’s, and monitored his health to see the effects.

Level 6: Observed and Confirmed Independently Under Identical, Controlled Conditions

By Level 6, we are starting to see some rigour in the analysis. An example might be Alcoholics Anonymous (AA) if they released their statistics. As an aside, a Stanford researcher did confirm in 2020 that AA is more effective at keeping people sober than therapy.

Level 7: Published in a Peer-Reviewed Journal

Even when a study is peer-reviewed and published, it can be wrong or misleading. A great example is the Wakefield Vaccine-Autism study published in The Lancet in 1998. With evidence of fraud, the paper was retracted in 2010. Dr. Bernstein’s Diabetes Solution meets this level because of its publication of results in Pediatrics.

Level 8: Published in a Peer-Reviewed Journal, Conducted in a Double-Blind Study and/or with a Control Group (When Ethical/Appropriate)

Let us explain some of these terms by pretending we are testing a new drug. A control group is a group of people, similar in characteristics to the active group who do not receive the drug (or a placebo, explained below). The control group allows us to compare the fates of the control group to the group receiving the drug.

A double-blind study is when the subjects of the study AND the people conducting the study do not know if the subjects are receiving the drug or a fake version (sometimes called a placebo). Why such extreme measures? To remove bias from the experiment and, in the case of a triple-blind study, from the analysis of the data afterwards. The “placebo effect” is probably the most famous form of bias being addressed in this kind of setup.

Generally, if a paper is published following this level of protocol, it likely has medical findings worth further investigation.

Level 9: The Same as Level 8 Plus the Results are Statistically Significant

Claims are often made in science but they do not always have a necessary level of statistical significance to back them up. Understanding p-values and confidence intervals are key in seeing what data are valid and which are not. Often journalists are not well versed in such things and will publish “breakthroughs” where there are none.

Level 10: The Same as Level 9 Plus Verified By Independent Third Parties

Anything meeting this standard can be considered, in my opinion, medicine. One study is compelling but multiple independent studies, being conducted under the strictest of conditions is more compelling. In most developed countries, all vaccines and medications have achieved this level of scrutiny, as a minimum, before being released onto the population.

Using the Levels

Pharmacies (chemists) sell pretty much anything from Level 2 or above e.g. ear candles. Alternative medicine generally gets up to Level 6 or 7 because, beyond that, it starts becoming actual medicine.

This is not to say alternative medicine or diets are bad or wrong, they are just not scientifically proven to the level of other potential treatments. In the absence of other verified treatments or as an adjunct to other therapies, they may be worth considering.

Journalists/mainstream media generally publish “breakthroughs” down to Level 7 but, I would argue it is only in the public interest at Level 9 or 10. Wakefield’s autism claims are the poster child for why this is the case.

Medical research can get to Level 9 and then hit a dead end because it cannot be easily replicated. The famous Rat Park experiments of the late 70s is a good example. While linking environmental conditions to addictive behaviours, the results proved difficult to replicate elsewhere.

In terms of general science, the same ten levels apply although the need for control groups and double blind studies are less important in other science areas. A good example of a Level 9 physics error was the 1989 cold fusion hoax highlighting that science is indeed fallible, at all levels, and before embracing something you read on the internet, ensure it meets the highest possible standard.


Here I present ten levels for assessing medical information. The ten levels are:

  • Level 1: Idle Speculation
  • Level 2: Secondary Source Anecdote
  • Level 3: Primary Source Anecdote
  • Level 4: Multiple, Corroborating Anecdotes
  • Level 5: Observed Under Controlled Conditions
  • Level 6: Observed and Confirmed Independently Under Identical, Controlled Conditions
  • Level 7: Published in a Peer-Reviewed Journal
  • Level 8: Published in a Peer-Reviewed Journal, Conducted in a Double-Blind Study and/or with a Control Group (When Ethical/Appropriate)
  • Level 9: The Same as Level 8 Plus the Results are Statistically Significant
  • Level 10: The Same as Level 9 Plus Verified By Independent Third Parties

Using these levels as a guide we can assess the medical information we receive and how reliable it may be.

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.

My Sixth Diaversary and Why I No Longer Need to Let My Hair Down

Last year I wrote about giving myself permission to let my hair down and the risk of mental stress and burnout which can come from obsessing with food. Today is again my diaversary and I write this in the early hours (travelling to ATTD 2023 in Berlin last week has made me an early riser) with the day ahead of me. Am I going to go big this year and “release the steam valve”? Actually, no.

A Year Is A Long Time In Diabetes

A lot has changed in the last 12 months. A little under a month after writing last year’s article I got my HbA1c results and they were trending the wrong way. While my HbA1c was “only” 6.6%, I had already drawn my line in the sand as 6.4% with 7.0% at the outside so I began using insulin. I went through the usual process of working out my “numbers” but really did not find success in multiple daily injections and, by the start of August, began looping via AndroidAPS. Today I am using a Dexcom G6 and an Ypsopump, connected via CamAPS. It has been quite the journey.

So Why No Blowout?

The fact of the matter is, for me, the looping rig has removed almost all the management. Pre-insulin, especially towards the end of that phase, every meal had an element of stress to it. I follow a lowish carbohydrate regimen (I do not carb count but avoid the foods which will spike me or eat them in moderation) but, despite this, was seeing big numbers. Using multiple daily injections was not much better. Estimating carbs was not a big deal but getting enough insulin in to get past the insulin resistance AND keep the numbers flat was difficult with lots of sugar surfing.

Looping has addressed all of this. With some residual pancreatic function still in play I can set the pump looping and do not even have to declare meals. If I stray from the path of lowish carbohydrate my numbers go up but the loop responds and they soon come down again relatively quickly. The only management I do is an insulin cartridge change every couple of days and an infusion set change every 3-4 days. I check in on my glucose levels throughout the day but this is more curiosity than anything else.

I literally go for hours every day without thinking about diabetes, seriously.

Without the shadow of diabetes hanging over me, the latent stress is gone and, while a milestone of sorts, today is just another day. I imagine, as it is Friday, we will go out for a nice meal but I doubt I’ll go much more wayward than having a cheeky dessert.

What About Your Numbers?

It is well and good lauding looping but the proof is in the low-carb pudding. How are my numbers holding up? I had an HbA1c test a couple of weeks ago and it came back as 5.5% which is a great turnaround. My Time in Range (70-180mg/L 3.9-10 mmol/L) moves between 90-95% and my Tight Time in Range (70-140mg/L 3.9-7.8 mmol/L) generally sits between 75-80%. I will be writing a separate article on Tight Time in Range which was a big topic of conversation at ATTD 2023.

Is there room for improvement? Absolutely, and with zero hypos (I did stray a little low once with all the walking around Berlin but generally none ever) I am slowly making the loop more aggressive by setting a lower target (currently 5.4 mmol/dL) and adjusting the IC ratio.

Why Looping Is So Exciting

Burnout is a big deal for people with diabetes. Suicide is a big deal for people with diabetes. I could quote numbers but you can Google just as well as I can.

While it should be noted that there was much talk at ATTD 2023 of the next generation of looping eliminating the need to bolus for food, I am an exception. The majority of the loopers I know, who have little to no pancreatic function, still need to declare meals/bolus. Nonetheless, if looping can bring some of the mental relief to others as it has to me I have no doubt it will impact suicide rates, meaning less dead people with diabetes and also impact burnout rates meaning better control/management and less long term complications. The jury will be out for literally decades on whether my hypothesis is true or not but I am hopeful.

Next Year?

It is hard to say. I have no doubt my residual pancreatic function will dwindle over time but with the pump pouring insulin into my body, my pancreas is working a lot less than it did a year ago which should help maintain it. Assuming my pancreas holds up, my seventh diaversary should be similar to this one. If not, by then the looping algorithms will have improved and, as I am still using Novorapid, there is also the option of the faster acting insulins to assist. I am hopeful for my future and the future of all people with diabetes.

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.

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!