Insulin Cooling Battles: Frio vs Gel

David Burren recently put me on to Breezy Packs which, if their claims are to be believed, offer a new way to keep insulin cool in the field. I have ordered a couple of Breezy Packs to put them through their paces but, first, I thought I would try out the existing methods commonly employed to show how they work.

Gel

Gel packs contain gel (no surprise there) which holds its temperature well and acts as an insulator. There is no actual cooling mechanism here other than the gel slows heat passing from one side to the other. So, to use a gel pack, you cool it down in the fridge (not the freezer as insulin does not like to be frozen) and put your insulin inside it to protect it from outside fluctuations in temperature. Outside heat is slow to heat up the gel pack which means the insulin stays cold.

Frio

Frio is, arguably, the most popular brand name for evaporative cooling pouches for keeping insulin cool. There are other brands out there (I even sell a version in my Etsy store) so feel free to shop around. They all work in the same way though. You immerse the pouch in water for, say, five minutes and it puffs up. You take it out of the water, wipe it down and put your insulin inside.

Not only are the pouch contents (generally silica gel beads or similar) an insulator but they are spectacular at absorbing and holding on to water. How Frio bags work is, when exposed to a warm temperature, the water in the beads begins to evaporate but evaporating water molecules takes energy so, instead of the external heat being used to raise the temperature of the water, some of it is used to turn the water to steam. This means the water temperature stays reasonably stable and, in turn, so does the temperature of the insulin inside the pouch. Our bodies use the same trick to stay cool when we sweat.

Breezy Packs

Breezy Packs offer a new way to keep insulin cool, which is similar to Frio bags but, instead of absorbing energy, turning water from liquid to a gas, it converts its active material from a solid to a liquid. No need to soak and wipe down. The physics of Breezy Packs is actually very smart so I will save it for when the pouches arrive and I will write another blog on the subject.

The Cooling Battleground: My Oven

It turns out that I can get my fan-forced oven down to around 30-40 degrees Celsius (104 degrees Fahrenheit) so this was my “controlled environment”. The contestants were a small Frio pouch capable of holding two insulin pens and a massive pillow gel insert.

The insert is 30x40cm with three panels. Both pouches went onto an oven tray with baking paper underneath to try and insulate from the metal bottom.

The gel pad was folded into three with two of the panels at the bottom and both pouches had a temperature probe put in the middle of them. As indicated above, the gel pad had been stored in the fridge whereas the Frio was soaked in tap water.

Once in the oven, I monitored their temperature and the temperature of the oven.

Here the gel pack is 10.7 degrees Celsius, the Frio pouch is 23.8 degrees Celsius, and the oven is 35.5 degrees celsius.

The Results

Thanks to the magic of Excel we can see how the two pouches fared. The oven temperature, which had previously reached the target temperature, was slowly dropping but remained above 30 degrees for the whole time. The Frio pouch, with the oven’s heat being used to turn the Frio’s water to steam, was holding a reasonably even temperature. The gel pouch, with nothing but insulation, slowly increased in temperature, catching up to the Frio after about 30 minutes, despite the 15 degree head start.

To be honest I was not sure the Frio pouch would work as well as it did as the oven was closed and, therefore, once the air inside the oven was saturated with moisture, the Frio would no longer be able to cool but for the 30 minutes it continued to work.

Conclusions

First of all I was really impressed the results came out as well as they did, showing the characteristics of the two pouches. For my money, if I was expecting to carry insulin for an extended period of time in high heat, I would likely look to a pouch that uses evaporative cooling. I would also invest in a MedAngel so I could check the temperature inside the pouch at any time and be alerted if things were going astray. Gel is a much cheaper option, of course, so, for short excursions, it will work fine. You could also, if you had a large enough pouch, put a cooled gel pouch inside a Frio pouch and gain a double benefit. As long as the Frio pouch is on the outside this should work fine.

EASD 2021: Reconciling the International Consensus Reports for LADA and Type 1. Part 2: Treatment

For Part 1, looking at reconciling the reports for diagnosis, go here.

Thanks to the generosity of #dedoc°, I recently had the privilege of virtually attending the world’s largest Diabetes conference: EASD 2021. Arguably the biggest news at the conference was an international consensus on the diagnosis, treatment, and management of Type 1 Diabetes. Interestingly, last year an international consensus was released for the diagnosis, treatment, and management of LADA. In Part 1 I reviewed how the two differed in terms of the diagnosis of Type 1 and LADA. In this second and final part I will look at the two reports’ recommendations for treatment and consider questions such as:

  • Should someone diagnosed with LADA go onto insulin immediately?
  • Are there treatments for Type 1 other than insulin?
  • If I do use insulin what are the pros and cons of the various methods of delivery?

As usual, for those who want the short version, you can go to the tl;dr section at the end.

Where We Landed In Part 1

In Part 1, I concluded the diagnosis flow chart from the Type 1 report was the more detailed and effectively covered LADAs flow chart.

So, assuming someone has LADA or Type 1 diabetes means either:

  • We have some reason to suspect diabetes (unintentional weight loss, ketoacidosis, glucose > 20 mmol/L (>360 mg/DL) etc.)

AND

  • Auto-antibody presence OR
  • Low C-peptide (less than 200 pmol/L (0.2 nmol/L) ) OR
  • No features of Type 2 diabetes (BMI >= 25 kg/m^2, no weight loss, no ketoacidosis, less severe hyperglycaemia etc.)

Treatment According to the LADA Report

The LADA report has a flow chart for treatment which looks like this:

The Type 1 C-peptide limit is different (0.2 nmol/L vs 0.3 nmol/L) but, given there are two other options available which do not consider the C-peptide level in the Type 1 report (auto-antibody presence and no Type 2 features), there is still the possibility that someone with Type 1 could have a C-peptide in any of the above three ranges.

I go through the LADA and Type 2 guidelines in detail in my “Gold Standard” LADA article. In short, if your C-peptide is over 0.7nmol/L (700 pmol/L) options include:

  • Metformin
  • GLP-1 RA
  • SGLT-2i
  • DPP-4i
  • Basal insulin
  • TZD

While part of the Type 2 algorithm, there is a notable exception of Sulfonylureas not being used with LADAs because “The panel concluded that sulfonylureas are not recommended for the treatment of LADA, as deterioration of b-cell function as a consequence of this treatment cannot be ruled out”.

For patients with a C-peptide below 0.7 nmol/L, there are two flow charts. The first is if heart (ASCVD/HF) or kidney (CKD) disease is present with the same medications as before except TZDs which may have been excluded because of the limited evidence of benefit and increased risk of bone fracture.

For patients without heart or kidney disease, we have this chart where the SUs are still not present but which does include TZDs.

What is good is this set of flow charts covers the entire Type 1 C-peptide spectrum which means, even when someone with LADA becomes a “classic” Type 1 because of declining C-peptide levels, we have a prescribed course of action. What is missing is a complete answer to the question “When should someone with LADA start using insulin?” The answer from the above flow charts is “If the HbA1c is above target” but no target is firmly established. Let us move to the Type 1 report.

Treatment According to the Type 1 Report

In fact, the Type 1 report immediately addresses the issue of targets for Type 1 in their first table.

Here the target HbA1c is 7.0% with the caveat that “all glycemic targets should be individualized and agreed with the person with diabetes.” So, unless we have discussed and agreed on a different target with our health care team, achieving an HbA1c equal to or below 7.0% is a good benchmark for considering moving to the use of insulin. This is in agreement in my post where I considered how high someone’s HbA1c could be before a significant risk of long term damage.

For the specific question of when someone with LADA should consider bolus insulin, we also have guidelines for post-prandial (after meal) insulin levels with the suggestion that 1-2 hours after a meal a person’s glucose level should be less than 10 mmol/L (180 mg/dL) and the option of pushing this to less than 7.8 mmol/L (140 mg/dL) if safe to do so.

In contrast to the LADA report, the Type 1 report takes an “insulin-first” approach saying “The cornerstone of type 1 diabetes
therapy is insulin replacement” and providing the following summary of the multi-pronged approach suggested for the newly diagnosed.

Given how difficult it can be to manage insulin therapy in the newly diagnosed, it acknowledges the need to prepare for hyperglycemia (“highs”) and hypoglycemia (“lows”).

The Type 1 report also talks about the relative merits for the different ways of delivering insulin.

Where money is no object, clearly, closed-loop technology is the winner.

Eventually (page 27 out of 37 pages), the Type 1 report talks about “Adjunctive therapies”. In other words, treatments which can be used alongside insulin.

There is common ground between the two reports with both reports mentioning Metformin, GLP-1 RA, and SGLT-2i. It also mentions pramlintide which is an amylin analogue (another hormone produced by the beta cells and, therefore, compromised in Type 1 diabetes). It fails to mention DPP4i and TZD. TZD may be because of the limited evidence but I am not sure why DDP4i’s were left off the list. They affect the same hormone cycle as GLP-1 RAs and therefore have similar effects/benefits.

Reconciling the Two Reports

In contrast to Part 1 where I sided with the Type 1 flow chart for diagnosis, here I am siding with the LADA report for treatment. There are a few reasons for this:

  • It explicitly considers treatment in the presence of heart and kidney disease
  • It offers a more comprehensive range of non-insulin treatment options e.g. DPP4i and TZD (but should likely include Pramlintide as well)
  • It takes the approach that insulin may not be necessary in patients with high C-peptide levels and, given the inherent hypo/hyper risk that comes with using insulin, if target ranges can be maintained, this seems like a sensible approach to me

This being said, the Type 1 report is much more comprehensive in considering the various ways of delivering insulin to the body (injection, pumps etc.) and also has a lot to say about looking beyond medication for individualised treatment e.g. considering lifestyle factors and diabetes education.

One big takeaway for all people with Type 1 or LADA should be that treatment no longer begins and ends with insulin. There are a range of other medications which can help with managing long term blood glucose levels and have other benefits such as helping a patient lose weight or reduce blood pressure.

tl;dr

Arguably, the LADA report’s flow charts for the treatment of Type 1 diabetes are more detailed for treatment than what is presented in the Type 1 report. Not only, does the LADA report consider insulin independence for patients with high C-peptide levels, it considers which medications are appropriate in the presence of heart or kidney disease. However, the Type 1 report fills in a significant gap of providing target values to chase and which help inform decisions such as when to move to insulin therapy.

The Type 1 report also goes into more detail in the areas of:

  • The relative merits and costs of different insulin delivery methods
  • Treatment of Type 1 diabetes beyond medication e.g. lifestyle factors and education

EASD 2021: Reconciling the International Consensus Reports for LADA and Type 1. Part 1: Diagnosis

Thanks to the generosity of #dedoc°, I recently had the privilege of virtually attending the world’s largest Diabetes conference: EASD 2021. Arguably the biggest news at the conference was an international consensus on the diagnosis, treatment, and management of Type 1 Diabetes. This is a comprehensive guide, backed by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD), which should, in my opinion, be the bible for health care professionals and for guiding health-related government policy.

Interestingly, last year an international consensus was released for the diagnosis, treatment, and management of LADA. I wrote a blog on it at the time going through the details. While not the same authors, nor directly endorsed by ADA/EASD, one would hope the two reports are broadly aligned in their approach given LADA is usually considered a sub-group of Type 1. In fact they are but there are differences at the edges and I raised this during the conference.

Rather than wait for the academics I thought I would go through the reports and see if I could make some headway. I will split this up into at least two parts with this one covering the diagnosis of Type 1 and of LADA.

As usual, there is the tl;dr section at the end for those that want to cut to the chase.

Diagnosing LADA

The first problem is there is no simple categorical feature of LADA. At diagnosis it shares features with “classic” Type 1 and Type 2.

So, while we can make a good guess at diagnosis, there is room for error. The report goes on to weigh up the various factors which can be used for assessment and comes up with the following flow chart.

So, first we test for the auto-antibody GADA. If it is positive, the person has Type 1 (LADA) diabetes and their treatment is then determined by their C-peptide levels. The report is vague on whether the C-peptide test is fasting, random time, or post-prandial (after a meal).

If the GADA screening is negative, the report suggests it is likely the patient has Type 2 diabetes and, therefore not LADA (although Type 3c and MODY should be considered). However, if LADA is still suspected, other auto-antibodies such as IA-2A, ICA, and ZnT8A can be screened. If these are positive, we are back to a positive diagnosis of Type 1 with treatment being defined by C-peptide levels.

Diagnosing Type 1

The Type 1 report also weighs up the various factors in diagnosing Type 1 compared to other Types, such as Type 2 and MODY and comes up with this flow chart.

The first statement, like the LADA report, is that no one feature is categorical, not even auto-antibodies (which can be present in other conditions). So, assuming something, such as DKA, has triggered the investigation, testing for auto-antibodies is, like the LADA report, the first place to look. Also, similar to the LADA report, the Type 1 report considers GADA as the first auto-antibody to screen for, followed by the others. If the test is positive, the patient is considered to have Type 1 diabetes.

If the test is negative (as can be the case in 5-10% of people with Type 1), age is the next consideration. For patients over 35 years old, it is not obvious what Type of diabetes they have. The suggestion is assume Type 2 unless there is suspicion of a different Type e.g. Type 3c, but monitor closely for a rapid deterioration in insulin production. After 3 years, test their C-peptide levels (“a random C-peptide measurement (with concurrent glucose) within 5 hours of eating”) and if they are very low (less than 200pmol/L) then they are considered to have Type 1 diabetes. If the C-peptide levels are high (greater than 600pmol/L) the patient is considered to have Type 2 diabetes. If their C-peptide levels are between these two extremes, the recommendation is to re-test in 5 or more years.

For patients who test negative for auto-antibodies and are less than 35 years old, if MODY is suspected, test the C-peptide and if greater than 200pmol/L, perform genetic testing for MODY. If the C-peptide is less than 200pmol/L, the patient is considered to have Type 1 diabetes. Where MODY is not suspected, and there are no indications of “classic” Type 2, the patient is considered to have Type 1 diabetes. While not obvious what the conclusion is for patients with a C-peptide greater than 200pmol/L, one would assume they follow the same path as those over the age of 35.

Reconciling the Two Consensus Reports

The Type 1 flow chart is more complex so we will use this as the foundation and modify it, if required, to align with the LADA flow chart.

In both reports it is directly acknowledged there is no categorical feature to define Type 1 or LADA. For the purposes of diagnosis, this means there must be a reason we are testing for diabetes in the first place. The Type 1 report suggests “unintentional weight loss, ketoacidosis, and glucose >20 mmol/L (>360 mg/dL) at presentation…Other features classically associated with type 1 diabetes, such as ketosis without acidosis, osmotic symptoms, family history, or a history of autoimmune diseases are weak discriminators.”

Assuming some kind of indicator of diabetes is in place, both reports call for screening for the GADA auto-antibody. If this fails, following up with the other indicative auto-antibodies. If any of these are positive then we have a diagnosis of Type 1 and, depending on the C-peptide level, treatment may differ. Given we are dealing with diagnosis and not treatment in this post, let us move to the case of a negative auto-antibody test.

For the LADA consensus report, once all of the auto-antibody tests come back negative, the conclusion is the patient is Type 2. However, the Type 1 consensus report does not give up so easily. As mentioned in the previous section, if the person is under 35, and there is no indication of MODY or Type 2 (high BMI, no DKA and less severe hyperglycaemia), the conclusion is the patient is likely to have Type 1 diabetes.

This last part, where the patient is negative for auto-antibodies, is probably the biggest departure in diagnosis between the two flow charts. Given there is a far higher rate of misdiagnosis of Type 1/LADAs as Type 2 than the other way around, my preference would be to side with the Type 1 report’s process and conclusions. As we will see in my future post on the treatment recommendations of the two reports, the treatment for a person with Type 1 and high C-peptide levels (as can be the case for LADAs), and the treatment for people with Type 2 is quite similar with main difference being the exclusion of sulfonylureas which can accelerate a person with LADA’s progression to insulin dependence.

tl;dr

The two consensus reports are pretty similar with the Type 1 report being the more comprehensive. The main difference is for people who test negative for auto-antibodies. For the LADA consensus report, it is assumed they have Type 2 diabetes whereas the Type 1 consensus report assumes, if there are no indications of MODY or Type 2, the patient likely has Type 1 and should be treated accordingly.

Therefore, whether someone is suspected of having Type 1 diabetes or are part of the LADA sub-group, the Type 1 consensus report’s flow chart is a good guide for accurate diagnosis. The main steps of this flow are:

  • Determine there is a reason to suspect some form or diabetes e.g. unintentional weight loss, ketoacidosis, and glucose >20 mmol/L (>360 mg/dL) at presentation
  • Screen for GADA auto-antibodies
    • If positive, the patient has Type 1 diabetes
    • If negative and under 35
      • Consider the possibility of MODY and, for a sufficiently high C-peptide level, test if suspected. If negative for MODY (presumably) treat them as if they were negative and over 35 (see below)
      • Consider the possibility of Type 2. If the presentation is consistent with Type 2 (high BMI, no DKA and less severe hyperglycaemia) then diagnose them as if they were negative and over 35 (see below)
      • If the presentation is not consistent with “classic” Type 2 diabetes, assume they are Type 1 and treat accordingly
    • If negative and over 35
      • Consider the possibility of other Types but, if there are no other indicators, assume Type 2 diabetes but monitor closely for a rapid drop in insulin production. Test C-peptide levels in 3 years (“a random C-peptide measurement (with concurrent glucose) within 5 hours of eating”). If the C-peptide levels are high, the patient is considered to have Type 2 diabetes, otherwise re-test in 5 or more years

Strawberry Cordial/Syrup

This time I had close to a kilo (2lbs) of strawberries about to turn and I have plenty of jam so I turned my hand to making strawberry cordial/syrup for friendlier strawberry milkshakes or maybe put some aside with gelatine to make a strawberry jelly (jello).

Ingredients

  • 1kg of Strawberries (about 2lb)
  • 1 litre of water (about a quart)
  • 1/8 tsp of pure Sucralose (equivalent to 1-2 cups of sugar)

Instructions

  • Rinse the strawberries
  • Hull them (cut out the tops and stems with a paring knife)
  • Halve them
  • Place the halved strawberries into a medium saucepan
  • Cover them with the water and bring the water to a boil. As soon as it reaches a boil, reduce to a simmer for 20 minutes
  • Skim off the foam. The strawberries’ colour will have transferred to the water
  • Remove the pot from the heat and strain through a fine mesh strainer. Do not press the solids or the liquid will become cloudy
  • Discard the solids, add the sweetener to the liquid and bring it back to a boil. Then reduce the heat to a simmer for 5 minutes
  • Skim any more foam to leave a clear deep red liquid.
  • Remove from the heat, allow to cool and pour into a container
  • Refrigerated, the syrup should last several weeks

I have thought about adding some lemon juice as a preservative so the syrup can be held, unfrigerated, in a dispenser bottle but I will try that next time.

Before taking this photo, I played with ratios and found an authentic strawberry flavoured milkshake needed a 1 (cordial) :2 (milk) ratio or thereabouts. In total it made around 600-700mL (a bit over a pint) which surprised me given the recipe called for 1 litre of water but maybe some evaporated and got strained out.

How Many Carbs?

If we calculate, based on the sugar content of the strawberries, we get an estimate of around 60g of sugars in 600mL of cordial. However, this assumes all the sugars from the strawberries was transferred to the cordial and did not get thrown away with the solids.

To get around this I thought I would see what my glucometer said and it came back with a sugar concentration of 32 mmol/L. Using the usual conversion tables this gives us around 575 mg/dL or 6g/L, much less that the upper limit. However, a glucometer only reacts with glucose, not fructose, or sucrose which strawberries also contain in roughly the same levels. So, to be on the safe side, I would probably triple this figure to give us 18g/L.

The good news we do not use a litre of this stuff to make a milkshake or flavour our mineral water. Let us say we go with a 1:2 ratio, as mentioned earlier. For a cup of liquid this means around 80mL of cordial to 160mL of liquid. Using the above concentration of sugars, this means the cordial contributes 1-2 grams of sugars to our drink which is not bad.

Making Strawberry Jam

This weekend we had surplus strawberries in the kitchen so I adapted the Mandarin Marmalade recipe to make Strawberry Jam.

Ingredients

  • 500g of Strawberries (with the tops cut off) (about 17oz)
  • 30mL Lemon Juice (1oz)
  • 500mL of Water (about half a quart)
  • 1/8 tsp of Pure Sucralose (equivalent to 1-2 cups of Sugar)
  • Enough gelatine to set 500mL (half a quart)

I have scaled the sugar down a little from the marmalade recipe as I found it a little sweet

Instructions

  • Wash the strawberries to remove any loose leaves or dirt
  • Simmer strawberries for 20 mins in half the water
  • If you have a gelatine sheet or powder, put it in the other half of the water
  • Mash the strawberries in the pan with a masher
  • Add the lemon juice, sucralose and gelatine
  • Simmer (do not boil) for another 20 minutes
  • While simmering sterilise your jar (this made enough jam for one jar) and then spoon in
  • Put in the fridge to set (maybe a couple of hours)

The simmering time was reduced from the marmalade recipe because strawberries break down much more easily than chopped mandarins. Also, I did not add the gelatine powder directly to the pan because it tended to clump up whereas adding it to the water and incorporating as a liquid gave a more even result.

The end result was this (the foam on the top settles down once cooled). The taste was not completely the same as a traditional jam but certainly an excellent approximation.

How Many Carbs?

150g (5.3oz) of strawberries have 2g of fiber and 9g of simple sugars. As before, I will exclude fiber from the calculations.

Scaling up for 500g, we are looking at around 30g of sugars. With 30 servings per jar, that is 1g per serving which is better than the mandarin marmalade!

Making Mandarin Marmalade

I tried my hand at making marmalade with some surplus mandarins and it turned out really well so I thought I would blog about it.

Ingredients

  • 800g of mandarins (about 12 for me)
  • 45 mL lemon juice (1.5 oz)
  • 800mL water (a little less than 1 quart)
  • 1/2 tsp pure sucralose (roughly equivalent to 4 cups of sugar, adjust to taste or use your preferred sweetener equivalent to roughly 4 cups of sugar)
  • Enough gelatine to set 1L (1 quart)

Instructions

  • Scrub the mandarins to remove wax/dirt/etc.
  • Simmer whole mandarins for 45 mins in the water
  • Remove the mandarins from the water and put the water to the side
  • Chop mandarins finely removing pips
  • Add back to the water with the lemon juice, sucralose and gelatine
  • Simmer (do not boil) and pick out any pips.
  • While simmering sterilise your jars (this made enough marmalade for three jars for me) and then spoon in
  • Put in the fridge to set (maybe a couple of hours)

What you will end up with is something that looks like this.

No photo description available.

How Many Carbs?

Mandarins have around 2g of fiber and 11g of sugars. As fiber is, by definition, indigestible, I will exclude it in the calculations but feel free to do what works for you.

In total I used 12 mandarins.

May be an image of food and indoor

Which means a total of 132g of sugars. Divided by three jars, that is 44g per jar. Assuming roughly 30 servings per jar we get about 1.5g of sugar per serving. In comparison, ‘normal’ jam has about 10g of sugar per serving.

Variations

While I used pure sucralose, you can use whatever sweetener you want. However, gelatine (or a similar thickener such as agar) will be needed as, without the four cups of sugar, the fruit pectin will not activate and you will end up with something with the consistency of apple sauce.

In terms of the citrus used, any should work of a similar total weight for the recipe. You may need to increase simmering time at the end to break down the pith for other citrus (mandarins typically only have a small amount of pith making this a quick recipe for them).

Making Sugar-Free Sugar Syrup

This is my recipe for sugar syrup (without the sugar) which I use for the odd cocktail and for my morning coffee. Very easy to make and no blood sugar spikes.

The Inspiration

The inspiration for making it came from this great book “Better Cocktails Through Chemistry”. Written by Scott Reba, who has Type 1 Diabetes, it contains various recipes for cocktail mixers and cocktails without the sugar hit, including sugar syrup (sometimes called Simple Syrup), a mainstay of any cocktail bar.

In Scott’s case he uses Splenda which works really well. The main sweetening agent in Splenda is sucralose, a modified sugar which is hundreds of times sweeter than sugar and not recognised by the body as food so it never gets converted into blood glucose. Requiring such a small amount to give the same sweetness kick as real sugar, it also does not have the same “intestinal effects” as other modified sugar sweeteners. However, Splenda uses bulking agents so it measures the same by volume as real sugar. Those bulking agents are dextrose and maltodextrin which can spike blood sugars.

Using Pure Sucralose

While I originally used Splenda to make the sugar syrup, when it became difficult to buy in bulk from my local supermarket, I went online and found I could buy pure sucralose. This also eliminated the dextrose/maltodextrin sugar spike issue. This is the packet I bought on Amazon.

This is the 100g bag which is very roughly equivalent to 100 cups of sugar so I doubt I will be buying another for quite a while.

The history of sucralose is quite interesting in that is was discovered by accident in 1976 when a couple of chemists were exploring the properties of modified sugar molecules. When told to “test” one particular compound that had been created, the chemist thought he had been told to “taste” the compound, discovering its sweetness and thus sucralose was born.

While deemed safe by food administration bodies across the world, here is a good summary of the studies. In short there is no finding in humans to suggest there is a problem and, in terms of toxicity, you would need to consume pretty much the entire packet in one sitting to get there. Then again, eating 100 cups of sugar in one sitting probably would not do you too many favours either.

How To Make It

The standard recipe for simple syrup is a 1:1 ratio of sugar to water by volume e.g. 1 cup of sugar to 1 cup of water. For cocktails a 2:1 ratio is sometimes used. Work out how sweet for want your syrup and use the ratio that works. For sugar and Splenda the ratio is simple given Splenda measures the same as sugar by volume. For pure sucralose, without the bulking agent, things are different.

Here is what you will need to make your sugar-free sugar syrup.

Of course, we have the packet of sucralose. We also have a pump bottle for the final product and, in my case, a 1/8 teaspoon. Ideally it would be 1/16 but these can be quite tricky to find.

Why the small teaspoon? Because, as mentioned, sucralose is really, really sweet. The exact magnitude of sweetness varies in the literature so my recommendation is to see what works for you. Roughly speaking, one cup of sugar is somewhere between 1/16 teaspoon and 1/8 teaspoon of sucralose. Experiment and good luck 🙂

Once you have your 1 cup of water and (1 cup of sugar or 1 cup of Splenda or 1/16-1/8 tsp of sucralose), all you do is put them in a pot on the stove, heat the liquid until the sweetener has dissolved and you are done. Let is cool and fill your pump bottle.

For sugar, heating the water is necessary for dissolving, especially for sweeter syrup ratios but for Splenda (where the bulking agent dissolves easily) and for sucralose (where there is simply not that much to dissolve) it is less necessary. For sucralose you could probably combine in the pump bottle and agitate for the same result.

A word of warning, because sucralose powder is so fine and so sweet, it will get on your fingers and they will taste sweet for at least a few hours after making this. Also, you are welcome to try other sweeteners like stevia/erythritol mixtures but my experience is they do not dissolve well into the water, form large crystals and impart little sweetness to the syrup. A pretty result with large crystals but useless for sweetening coffee.

The End Result and Variations

Once dissolved and poured into the pump bottle you are good to go. For me, the 1/8 tsp of sucralose to 1 cup of water gave a pump of syrup which was slightly sweeter than a tsp of sugar but for coffee and cocktails it worked well enough.

Once you have mastered sugar syrup, a range of options open up thanks to the flavourings available in the shops. Using this as a base, you could add banana or strawberry flavouring for milkshakes, or create a vanilla syrup for coffee. Given the sheer volume of syrup that can be created from a 50 or 100g packet of sucralose you have a lot of opportunity to explore.

ATTD 2021: Obesity Does Not Cause Type 2 Diabetes

ATTD 2021 was held 2-5 June, 2021 and, thanks to dedoc, I had the privilege to attend and live tweet the presentations. This post is not a distillation but a summary of one particular presentation by Dr. Walter Pories.

Dr. Pories pioneered gastric bypass surgery and was the first to document the surgery leading to the remission of Type 2 diabetes. He is a world leader on the interplay of the surgery and its effects on Type 2 Diabetes and he is of the firm opinion that, while the procedure does indeed put Type 2 diabetes in remission, it is not a result of the associated weight loss. Let me review what he presented.

Firstly, Dr. Pories talked about how he developed the modern gastric bypass procedure in North Carolina in the 1980s.

With the surgical procedure going well and developing a good reputation for reversing severe obesity, a Dr. Jose Caro requested one of his patients be considered for the surgery even though she had poorly controlled Type 2 diabetes despite using insulin to try and control it.

Dr Pories agreed to do the surgery but, the mystery was, a few days after surgery the patient had normal glucose levels and no longer needed insulin. The same thing happened with other Type 2 patients referred by Dr. Caro.

Neither Dr. Pories or Dr. Caro could explain it and blamed each other for poor diagnostics. For the fifth patient who underwent the procedure Dr. Pories showed how the blood glucose levels and insulin requirements progressed after surgery.

A patient whose blood glucose was 495 mg/dl (27.5 mmol/L) before surgery despite being given 90 units of insulin, within a week, the patient no longer required insulin with measured blood glucose within the standard range.

Over 9 years, Dr. Pories saw similar results with a remission rate of 83%.

It was consistently shown that Type 2 patients had a significant reduction in HbA1c and in the requirement for medication. The weight loss associated with the surgery could not explain it because weight loss simply did not happen that quickly. Moreover, other metabolic issues were shown to be reduced through the surgery.

To solve the mystery, Dr. Pories looked at other gastric surgeries and considered their remission rates. The conclusion was the more the gut was bypassed, the better the results. Dr. Pories formulated a hypothesis that the cause of Type 2 could be bad signalling from the gut with the surgery effectively turning the bad signal off.

Another piece in the puzzle came from studying the lactate levels of people with Type 2 diabetes which were shown to be higher. Dr. Pories characterised lactate as the black smoke seen coming from a poorly tuned engine. It meant the glucose was not being metabolised correctly under the action of insulin.

It was shown that, after surgery, the lactate issue went away suggesting whatever was fixing the Type 2 diabetes was also fixing the broken metabolism.

Looking at the glucose processing cycle (Cori cycle) more carefully it suggested that the gut signal hindered the glucose processing and instead of 36 ATP units (the energy “currency” of cells) being produced, only 4 were being produced and a lot of lactate.

Dr. Pories talked about evidence from Romania comparing cell response to blood taken before and after gastric surgery which confirms a difference in the blood and therefore potentially a difference in the chemical signals it contains. He then presented his conclusions.

Does obesity cause Type 2 diabetes? Whatever the cause of Type 2, obesity simply does not make sense because Type 2 goes into remission under gastric surgery before the fat is gone and it does not explain the different rates of remission for different kinds of surgery. While eating sugary foods only worsens the problem by thrashing the broken energy cycle, it is not the cause of the disease. In other words, characterising Type 2 diabetes as a “lifestyle disease” misses the mark. It is clear that there is something fundamentally broken in the body which is usually fixed when the gut is removed from the equation.

Rather than think of obesity and a person’s desire for high energy foods as the cause of their Type 2 diabetes, see it as a symptom of someone who has a broken energy production engine leading to excess production of body fat and a lack of energy for the cells causing the body to crave high energy foods to compensate which, unfortunately, only makes the problem worse.

People with Type 2 diabetes deserve our respect rather than our judgement and an understanding there is far more going on with them than a lack of exercise and poor food choices.

ATTD 2021 Distillations: Pregnancy for Women With Type1/Type2/Gestational Diabetes

ATTD 2021 was held 2-5 June, 2021 and, thanks to dedoc, I had the privilege to attend and live tweet the presentations. However, with many sessions running in parallel, it was not possible to attend every session and many sessions covered similar topics. To address this, I watched the recordings of the sessions I missed and brought them together under common themes. Where the presentation called out that the results were confidential and/or not yet published and not for distribution I left them out of my blog but I can confirm there was nothing presented under confidence which contradicted what I am presenting in these summaries.

This is my second “distillation” which is on pregnancy and gestational diabetes. For my first “distillation” covering looping go here. Firstly, let me be clear in my intent: I am a male and never been pregnant. My aim is to present the research, as I understand it, from ATTD 2021. I am not a medical professional and nothing here should be considered medical advice or me telling you what to do with your body. If something I have written is of interest, I strongly encourage you to discuss it with your medical team to determine the best course of action for you. I will also call out that this post discusses the possible adverse outcomes of pregnancy for the baby. Not all pregnancies go to plan and if this subject is distressing for you, you may be triggered by the contents of this post.

A common observation of women with diabetes is their menstrual cycle has a significant impact on their blood glucose level management. It is known in that the fluctuation of hormone levels in the body directly impacts insulin resistance. The same happens in pregnancy with the thinking being that hormone levels increase in a woman’s body to increase insulin resistance so more glucose reaches the baby for growth and development.

For women with diabetes this amplifies an existing health condition and, for women who do not have diabetes, given the stresses this puts the body under, it can trigger diabetes during pregnancy in what is called Gestational Diabetes. ATTD 2021 looked at some of the latest research on diabetes and pregnancy which I present here.

Specific questions covered in the presentations were:

As usual there is a TL;DR section at the end if you want to read the summary of results without the details.

If I am a Woman with Diabetes, What are the Health Risks to my Baby?

Dr. Helen Murphy presented on the risks to the baby for women with Type 1 and Type 2 diabetes. Both Dr. Murphy and other presenters made it clear that there has not been a lot of improvement in adverse outcomes for pregnant women with diabetes (congenital abnormalities and deaths), for decades. As we will see later, there is potential for this to change with tools like continuous glucose monitoring devices (CGMs).

A result which surprised me was the risks to women with Type 1 and Type 2 diabetes was about the same and slightly higher for neonatal death in women with Type 2 diabetes. While there appears to be a trend upwards in some of these graphs, Dr. Murphy pointed out the tending was not statistically significant.

A question I had was how this compares to the general population. Dr. Robert Lindsay, covered this with some earlier data. Assuming the risk level has remained the same for the general population, for stillbirth, women with diabetes have a risk somewhere between 2-4 times higher than the general population.

For adverse outcomes (death and congenital defects), we also see similar risk profiles for women with Type 1 and Type 2 diabetes. Literally 1 in 10 women with diabetes, who do not prepare for pregnancy, have a serious complication with their pregnancy. The good news is, with planning and preparation, this number goes down to 1 in 50 which is close to the rate for the general population.

Planning and preparation in this case means taking folic acid before and during pregnancy and controlling blood sugar levels as well as possible. The guideline is to have an HbA1c below 48mmol/mol (6.5%) which, for many, is easier said than done. Many factors affect a woman’s ability to control their blood sugars beyond the usual diet and exercise. For Type 1 is was shown that other factors include:

  • Age (younger Type 1s often struggle to control their average blood glucose levels compared to their older counterparts)
  • Social disadvantage (Deprivation)
  • Time since diagnosis (while older Type 1s typically have a lower HbA1c, those diagnosed 5 or more years ago typically have a higher HbA1c)
  • BMI (the higher the body mass index, the lower likelihood of a woman with Type 1 having an HbA1c below 6.5%)

For Type 2, social disadvantage was a factor although it does not seem to be as pronounced, time signce diagnosis was a factor, and also BMI.

The only one of these factors which can be easily addressed by a woman looking to get pregnant is BMI but, as many of us know, shifting the needle on weight is not a simple task.

While important before getting pregnant and in the first trimester, HbA1c is also predictive of adverse outcomes as late as the third trimester. For both women with Type 1 and Type 2 diabetes, the risks significantly increase for an HbA1c above 75 mmol/mol (9.0%). Dr. Murphy also pointed out that this result removed confounders (contributing factors such as weight, age etc.) In other words, HbA1c is very predictive of risk.

She also presented details of the relative risks for Neonatal Intensive Care (NICU) admission, preterm birth, and having a “big baby” (LGA: Large for Gestational Age) showing HbA1c also predicts for these events as well.

Dr. Robert Lindsay also presented on factors associated with still birth, including those with Gestational Diabetes.

Not surprisingly, blood glucose levels are a factor here as well even for those diagnosed and treated for gestational diabetes. Given there were different results for the diagnosed and undiagnosed, it suggests there is a factor beyond fasting blood glucose affecting outcomes which treatment is addressing. My assumption is it is the non-fasting glucose levels and their fluctuations.

While the results may seem depressing, there is hope. As we will see in the next two sections, there are new technologies and treatments which can positively impact pregnancy outcomes for women with diabetes of all Types.

Health Benefits of Using a CGM During Pregnancy

Dr. Jennifer Yamamoto presented on the performance and benefits of Continuous Glucose Monitoring (CGMs) for pregnant women with diabetes.

For sensor performance, she spoke of a study looking at placement on the body of the CGM sensor covering women of all Types.

Overall, regardless of placement the accuracy as measured by MARD (Mean Absolute Relative Difference), where a lower number indicates better accuracy, performance was good but the arm was the best area for placement achieving 8.7% accuracy.

In terms of the benefit of using a CGM device, Dr Helen Murphy, presented results specific to Type 1 showing, especially in the third trimester, significantly better results for pregnant women who used a CGM; they literally gained an additional 100 minutes per day in range.

As to be expected, this had a knock-on effect on neonatal outcomes with a statistically significant lowering of risk for a larger baby (LGA), hypoglycaemia, and neonatal ICU admission.

Dr. Claire Meek presented on the predictive power of CGMs for adverse outcomes, using CGM measures such as mean glucose levels (MEAN), Time in Range (TIR), Time Above Range (TAR), Time Below Range (TBR), the coefficient of variation (CV, a measure of variability of levels), and standard deviation (SD, another measure of variability). These were compared to the effectiveness of biomarkers in the blood and the traditional HbA1c.

Even for me these graphs are hard to read but the key takeaway is to look at the rows with an asterisk on the end as these are the measures which were significantly predictive. So, for preterm birth, for 12 weeks, CGM and one of the biomarkers fared well and for 24 weeks, CGM, a different biomarker, and HbA1c fared well.

Looking at predictors for a “big baby” (LGA), Neonatal ICU (NICU) admission, and Neonatal Hypoglycaemia (NH) we see CGM and HbA1c are the constant performers for prediction.

Looking at just Time in Range (TIR) and the HbA1c, we see, for early pregnancy a CGM offers good predictability and the opportunity for early intervention. Later in the pregnancy, HbA1c provides stronger predictability.

In conclusion, CGMs not only offer the ability to predict adverse outcomes early on in the pregnancy but their use can also improve them. Yet again we have compelling evidence that CGMs offer tremendous benefit to people with diabetes.

Should Women with Diabetes Take Metformin?

The final presentation I will talk about was a debate on the benefits and risks of using metformin during pregnancy. This was discussed between Professor Denice Feig who argued the “pro” case and Dr. Yariv Yogev who argued against. However, discussions in the question and answer session at the end showed both agreed on many of the key points with little debate between them.

Typically a “Type 2 drug”, metformin has many effects on the body but is broadly known as a drug which reduces insulin resistance. It is known to cross over the placenta into the baby but it is well established that it does not cause birth defects.

It is understandable that the issue of metformin use needs to be discussed/debated because international consensus on whether metformin or insulin should be used as a first line treatment is still mixed.

Professor Feig presented a meta-analysis study showing the benefit of metformin over insulin although it was clear in the discussion that many women with diabetes often used insulin as well as metformin to help control blood glucose levels.

Factors of potential concern were a lower gestational age i.e. babies were born slightly earlier and a slightly higher incidence of “small babies” or what is referred to as SGA (Small for Gestational Age).

Professor Feig presented other studies comparing metformin to insulin with similar results finding:

  • Metformin helped with glucose levels after meals
  • There were less cases of hypoglycaemia with metformin
  • Metformin reduced the amount of weight gain in mothers and the need for c-sections

For women with Type 2 we, again, saw similar results and the potential for a “small baby”.

There was discussion on the long term effects on the baby of exposure to metformin but both presenters agreed the results are mixed and no strong conclusions can yet be drawn that there are detrimental effects.

Dr. Yogev presented a great table showing how metformin compared to a placebo for outcomes (a p-value of 0.05 or less indicates statistical significance) indicating:

  • Lower birthweights with metformin
  • Reduction in the rate of extremely large babies
  • Increase in small babies

Dr. Yogev did point out that in his own studies he could not confirm the increased risk of small babies as a result of using metformin. Both agreed that where there was a risk of babies being born small, metformin may not be advisable e.g. in the case of twins. Professor Feig also made the point that, if a pregnant woman with diabetes was taking metformin and there was indications that her baby was undersized, metformin could be stopped during pregnancy to minimise any problems.

TL;DR

Health risks to the baby for women with diabetes are:

  • Higher risk of stillbirth and neonatal death (death in the first few weeks of birth) compared to the general population
  • Higher risk of congenital defects

These risks can be reduced through taking folic acid both before and during pregnancy and reducing HbA1c. However many factors affect the ability to reduce HbA1c such as age, social disadvantage, time since diagnosis, and weight. While difficult, HbA1c is a strong predictor of outcomes at all stages of pregnancy so any intervention which can help reduce HbA1c is of significant interest.

One intervention which shows promise are CGMs whose measures are predictive of outcomes (especially in early pregnancy while HbA1c is good at predicting outcomes later in pregnancy) and, therefore CGMs can be used to advise early intervention. It was also shown that the use of CGMs for women of all Types saw a reduction in adverse outcome risk. For pregnant women using a CGM while accuracy was good wherever the sensor was placed, placement on the arm showed the most accurate results.

A second intervention which shows promise is metformin, a drug which lowers insulin resistance. While reducing risk across most measures, there was potentially an increased risk in a smaller baby for gestational age (SGA). It was agreed that, in most cases, the risk could be managed through active monitoring of the pregnancy (and taking the pregnant woman off metformin if needed) but for cases where there was an existing known risk of SGA (twin birth, for example) metformin would not be advised.

ATTD 2021 Distillations: Looping

ATTD 2021 was held 2-5 June, 2021 and, thanks to dedoc, I had the privilege to attend and live tweet the presentations. However, with many sessions running in parallel, it was not possible to attend every session and many sessions covered similar topics. To address this, I watched the recordings of the sessions I missed and brought them together under common themes. Where the presentation called out that the results were confidential and/or not yet published and not for distribution I left them out of my blog but I can confirm there was nothing presented under confidence which contradicted what I am presenting in these summaries.

This is my first “distillation” covering looping. Once the domain of the online diabetes hacker community, looping (the interaction of a CGM and pump, with minimal human interaction to maintain blood glucose levels) has become mainstream with commercial looping systems available.

Specific questions covered in the presentations were:

As usual there is a TL;DR section at the end if you want to cut to the summary.

Loop vs Non-Loop: Which is Better?

Dr. Bruce Buckingham presented a study considering the Omnipod pump with Dexcom looping versus “Standard Treatment” i.e. non-looping options such as multiple daily injection or traditional pumping. For both children and adults, looping brought the HbA1c down to sub-7% levels. As we know from my previous analysis, getting the HbA1c below 7% is important to reduce the risk of long term complications.

Time in Range also improved with looping gaining more than two extra hours in range for the adults and almost four additional hours for the children.

Looping also helped with overnight highs in children and, to a lesser extent, with adults.

When looking at people with Type 2 Diabetes, there was also improvement in Time in Range and a reduction in Time Above Range.

Dr. Charlotte Boughton presented similar research showing a clear improvement of looping over multiple daily injection or a non-looping pump (HCL = Hybrid Closed Loop i.e. looping with declarations/human adjustments)

Dr. Goran Petrovski also provided details on the results of how Medtronic compared to multiple daily injection and CGM showing significant improvement in Time in Range and HbA1c.

He also showed the benefits of the closed loop system become apparent in the first few days of activation and continue to improve over the following months.

Loops Which Bolus vs Those Which Only Adjust Basal: Which is Better?

Dr. Ahmad Haidar provided a great summary of many studies comparing Automated Hybrid Closed Loop (AHCL) systems vs a “Sensor Augmented Pump with Predictive Low Glucose Management” (SAP + PLGM). To translate, comparing a system which gave “shots” of insulin against those which just managed continual rate of release (basal rate) e.g. Medtronic 780G vs 670G. As can be seen, regardless of the technology employed, the improvement was similar across the systems.

Dr. Anders L. Carlson spoke of the Medtronic study.

The results showed statistically significant improvements across all measures, including the time below range.

This was also backed up by a second and third study showing increased Time in Range and reduced time both above range and below range.

The first study showed stronger benefits for adolescents (also seen in the second study and shown on the Pivotal Study image above) and improved Time in Range (TIR) when a more aggressive endpoint was set.

The second study also confirmed the notion that a more aggressive endpoint led to an increased Time in Range and went further to suggest setting the Active Insulin Time to its lowest setting also helped improve Time in Range and lower Time Below Range.

Dr. Thekla von dem Burge showed that the benefits in the Medtronic system extended all the way down to pre-schoolers where there was a significant increase in those achieving range targets.

Dr. Boris Kovatchev presented a study using t:slim technology, instead of Medtronic, comparing Control-IQ to Basal-IQ which also confirmed a fully automated loop system improves Time in Range. It also showed the benefits begin within literally a month of employing the technology.

This study also showed the greatest benefit of the Control-IQ system was in the night time where, at its peak, it achieved 30% higher Time in Range.

As with the Medtronic studies, Time Above/Below Range was also improved with Control-IQ and also brought children’s metrics in line with the adults’.

In line with the Medtronic research, Dr. Jordan Pinsker showed the Control-IQ system provided benefit to all age groups, improving Time in Range and reducing Time Above Range.

Furthermore, the analysis showed those with poorer control (as measured by GMI, an estimation of HbA1c) achieved the greatest improvements. This is interesting because good control is often used as a selection for people to include in studies meaning much of the research could be missing a cohort who will benefit significantly from looping technology.

In line with Medtronic and t:slim, Erik Huneker also found found full looping had benefit over Predictive Low Glucose Suspend (PLGS, suspension of basal insulin when low) for people with highly unstable diabetes when using Diabeloop.

Finally, we had Dr. Roman Hovorka who, with the CamAPS looping setup, saw similar results to the other systems and great improvements in overnight management.

Is Faster Insulin Better in Closed Loop Systems?

Dr. Boris Kovatchev presented a paper comparing the performance of fast insulin to standard insulin in a fully closed loop system (no meal announcements) with moderate-vigorous exercise.

The results showed no significant difference between the standard insulin and fast insulin, as shown in the table below. Significance is indicated by the ‘P value’ in the last column. For statistical significance this value needs to be less than 0.05 which it was not across the key measures meaning the results were comparable between the two insulins and there were no significant differences.

Looking at the traces, we can see both insulins followed a similar path. The authors concluded that the algorithms were possibly better tuned to the standard insulin which gave it the comparable performance.

Dr. Roman Hovorka presented similar results when comparing Aspart and Fiasp in a CamAPS system. In terms of Time in Range and Mean Glucose, there was not a significant difference between the two insulins.

What is the Future of Looping?

Key areas of focus for looping research were highlighted across multiple talks. These include:

  • Tailored profiles for different kinds of people for better prediction (pregnancy, children, people with gastroparesis, men, women etc.)
  • Improved automation through machine learning of the individuals habits e.g. when they eat, exercise.
  • The use of additional sensors was also mentioned to aid the system predict exercise and meals
  • Simplified meal declarations to remove the need for carbohydrate counting i.e. declaring a meal and small/medium/large instead
  • The use of additional medications in the insulin mix to improve the looping system’s response to blood glucose changes e.g. SGLT-2 inhibitors, GLP-1 agonists, Amylin analogues

TL;DR

The key results of the research presented for loops were:

  • Looping vs Non-Loop
    • Benefits were seen in measures such as HbA1c and Time in Range
    • The benefits of looping systems were present in both adults and children
    • The benefits translated to literally hours per day additional Time in Range
    • The strongest improvements were seen overnight
    • People both with Type 1 and Type 2 diabetes saw improvement
    • Benefits were independent of the specific technology employed. It was looping in general which provided the gains
    • The benefits became apparent within weeks of looping being activated and were maintained for months
  • Looping with Bolus vs Looping which only adjusts Basal Rates
    • Pretty much the same results as for Looping vs Non-Loop, although there were no Type 2 studies presented
    • Setting a more aggressive target led to an improved Time in Range
    • Setting a smaller Active Insulin Time also improved Time in Range
    • The greatest improvements were seen in patients with the poorest control, as measured by HbA1c
  • Faster Insulins in Closed Loop Systems
    • Contrary to what we might predict, there is very little evidence that faster insulins provide significant benefit over slower insulins.
  • Future of Looping
    • Improved prediction through
      • Tailored, preset profiles for groups such as children and pregnant women
      • Machine learning the individual instead of the application of generic big data models
      • Additional sensors
    • Assuming they will be needed, simplified meal announcements to replace carbohydrate counting/declaration
    • Different insulin mixes to include other medications to help the looping system respond to things like exercise and meals