I am coming to the end of my EASD 2020 series but what a conference it was for announcements and discoveries. It just goes to show that research into diabetes is thriving and it suggests we have a lot to look forward to in the future with continual advancements in medical treatments and technology,
The presentations on Insulin Icodec, a once-weekly basal insulin, was particularly exciting for me.
What is Insulin Icodec?
Insulin has come a long way in the last 100 years. Early forms of insulin were derived from farm animals but, with the advance of recombinant DNA (the ability to combine DNA from multiple sources), it became possible to manufacture insulin with bacteria or yeast. Mass production of ‘regular’ insulin (human-equivalent) followed until the mid-nineties. In 1996 a new form of insulin was introduced to the market by Eli Lilly in the form of ‘Lispro rDNA’. This was a genetically modified form of insulin which acted in the body like regular insulin but with modified properties.
Since then, insulin analog advances have been made to improve how quickly a rapid-acting insulin takes to reach peak activity or, in the case of basal insulins, to make them last for longer and more consistently in the body.
Insulin Icodec is very much in this second camp. While some basal insulins, up until now, could work for around 24 hours, this is not the case for Icodec which makes basal insulin available to the body over a period of one week.
Presentations
Ulrike Hoevelmann presented on the effectiveness of the insulin. As we can see here, it maintains a relatively constant supply of insulin to the body over the seven days.
This particular research involved 50 participants and explored the properties of Icodec compared to Degludec (Tresiba).
Julio Rosenstock presented on a study with 247 participants comparing daily basal injections with weekly injections in a double-blind, double-dummy test. Once-weekly insulin Icodec showed a higher rate of hypoglycaemic events, compared to daily basal insulin glargine U100.
However, Doctor Rosenstock was quick to point out the only difference which was statistically significant was the Level 1 hypos. In all other aspects compared, there was no statistically significant difference in outcomes or adverse events between the two insulins.
Only For Type 2s?
Both studies focussed exclusively on Type 2s. So, will Icodec also work for other Types, such as Type 1? I asked this in one of the presentations and was told it certainly will but this is not where they are researching right now. My guess is the research trials are focussing on the majority of diabetics i.e. Type 2s. It is still early days so I am sure, before Icodec reaches the market, it will also be tested in Type 1s. While some Type 1s I know have expressed concerns around the risks of being subject to a bad dosing decision for an entire week, I think this would affect all patients about the same, regardless of Type. Personally, I think the more technology can remove the burden of management from diabetics and carers, the better and going from 365 injections a year down to 52, for me, is compelling.
Most diabetics, certainly most Type 1s, know their HbA1c. As described previously, the HbA1c is a measure of the ‘average sugariness’ of the blood over the last three months. As a diagnostic test, the HbA1c has been with us for a little over 40 years and has served us well. However, as technology has improved for the management of diabetes, the ways we keep tabs on our blood has also evolved and a new measure: Time in Range is growing in popularity among both diabetics and researchers alike. In fact, quite a few talks at EASD 2020 spoke directly at Time in Range and linked it to the long term health of diabetics.
In this blog I will talk about what the HbA1c tells us, its limitations, and the additional insights we can gain through the Time in Range. As usual, at the end, we have the ‘Cliff Notes’ tl;dr.
Benefits of Measuring the HbA1c
As I have shown previously, many papers examine the relationship between long term HbA1c and complications and this was reiterated by Professor Pratik Choudhary at EASD 2020 with this excellent graph.
The Limitations of the HbA1c
While of diagnostic value, there are limitations with the HbA1c measure.
Firstly, the measure assumes the lifespan of the red blood cells as part of the calculation so, if this assumption is invalid, it can affect the HbA1c. Common causes of false HbA1c readings include:
Anaemia
Excessive alcohol consumption
Excessive use of opiates
Pregnancy
Blood transfusions
The second and arguably biggest limitation of the HbA1c measure is while it speaks to the ‘average sugariness’ it says nothing of the variation. This is why, historically, doctors have erred on the side of a higher HbA1c with Type 1s, often to the diabetic’s frustration. The reason is simple: if the diabetic’s glucose levels fluctuate significantly, and they keep a lower glucose level, they are at an increased risk of hypo whose effects are immediate (groggy, fall over, go unconscious etc.) whereas running a little ‘high’ and fluctuating mitigates the risk of hypos at the expense of increasing the risk of long term nerve damage. To put it simply, with limited visibility of the fluctuations using strip glucose monitors, it makes sense that doctors err to minimize risk today and encourage their patients to run a little higher with a view that the longer term risks can be managed later.
Interestingly, in his talk, Professor Choudhary blamed his patients for running high and having high fluctuations, suggesting they are driven by fear of a hypo which then leads to over-correcting and large fluctuations.
Fortunately, Continuous Glucose Monitoring (CGM) technology gives a much clearer picture of the fluctuations, informing both the doctor and patient, and allowing them to proceed informed, rather than out of fear.
Time in Range
It has taken a while for a standardization of Time in Range guidelines but in August 2019 we got one from the ADA.
The standard is adjusted for various sub-groups of diabetics.
What is more, these ranges are not random, or at the whim of some group of doctors, but backed up by statistically significant links to both long term complications and the equivalent HbA1c.
As we can see, we can link changes in Time in Range to changes in the risk of retinopathy, microalbuminuria, microvascular complication, and nephropathy.
Professor Bergental also showed, using the CGM data of three of his patients, how much more insightful Time in Range can be, compared to HbA1c.
Here the three patients have exactly the same HbA1c but very different experiences with their diabetes. with the third patient spending a lot more time in hypo and much less time in range.
Bringing together the Time in Range with the latest data on what it means for long term complications, the Ambulatory Glucose Profile (AGP) Report captures the key metrics along with statistically significant guidelines in one page.
As we can see the report captures:
Percentage time in the different BGL ranges
Average Glucose
Glucose Management Indicator (GMI): an estimation of the HbA1c
Glucose variability: a measure of fluctuation
which, given the GMI on its own is a proxy for the HbA1c, shows we are considering a lot more information in the AGP report than simply ‘average sugariness’.
The Big Problem with Time in Range
The big problem with Time in Range analysis is it requires a Continuous Glucose Monitor (CGM) to capture the data: it is a metric of privilege. While many of us have no exposure to it in the West, in places like Mexico, some Type 1 diabetics are given literally one strip per day to manage their blood glucose levels. Fortunately, research shows the proportion of finger pricks in, below or above range can still be correlated to HbA1c estimations and, therefore, give an indication of long term risk.
Also, with an increase in Time in Range of 10% leading to a lowering of HbA1c of 0.5%, using the above, we see that for someone whose finger pricks are in range 70% of the time, this correlates to an HbA1c of roughly 7%, as with the CGM data. This means the AGP Report and the guidelines it contains can be applied to CGM or finger pricking equally. So, while a diabetic’s management technology may change over time, their reporting for their health care team does not have to.
tl;dr
With the advent of Continuous Glucose Monitoring (CGM) we have new and insightful ways of examining our data beyond what is available with finger pricking and the HbA1c. One such way is analysing the Time in Range of our data. Time in Range provides a wealth of knowledge about both average sugariness and the glucose level fluctuations. One way to present this information is with an Ambulatory Glucose Profile (AGP) Report.
While driven by CGM technology, it is possible to use the AGP Report with finger pricking by considering the proportion of results that fit in the low medium or high categories. This now means we have a common report whether we are finger pricking or using CGMs.
Clinical research using Time in Range analysis has also established that it is predictive for a range of long term diabetes complications. This means, Time in Range analysis and the AGP Report are now a viable alternative to the HbA1c and provide much richer insights.
I first read about incretin mimetics in Dr. Bernstein’s fourth edition of “Dr. Bernstein’s Diabetes Solution” published in 2011. He devotes nine pages to this class of drugs and it is a good place as any to describe what they do. It should be noted for those unfamiliar with Dr. Bernstein, his focus is on the treatment of Type 1 diabetes. While incretin mimetics are sometimes hailed as a ‘Type 2 drug’, they can be of benefit to diabetics of all Types as I will show in this blog.
From there I will update the information based on some peer reviewed papers from recent years (specifically relating to LADA) and the presentations at the recent EASD 2020 conference and then onto my own experience using them. As usual, there is the tl;dr at the end if you want the short version.
What Are Incretin Mimetics?
To understand the role of incretin mimetics, it is important to understand that beta cells produce, not just insulin, but also another hormone called amylin. Amylin is a ‘satiety’ hormone i.e. it makes you feel full. So, as the beta cells become damaged they are less able to produce this hormone. Amylin is released in response to the presence of ‘gut hormones’ in the blood, released when the gut is stretched. These ‘gut hormones’ are called ‘incretins’.
Based on all this, Dr. Bernstein suggests that diabetics of all persuasions are likely to not feel as ‘full’ as their non-diabetic counterparts. For me, this poses the question: “If someone who has impaired beta cell function cannot effectively regulate their eating, is it possible Type 2 diabetes and pre-diabetes causes obesity and not the other way around?” but that is a blog for another day.
At the time of writing his book, Dr. Bernstein describes incretin mimetics as being used to lower blood sugar levels after meals and for weight loss. We now know they have many more effects on the human body.
GLP-1 Receptor Agonists (GLP-1RAs) are one such incretin mimetic. As per the above we see, among other things, they can:
Lead to weight loss (likely due to the increased sensitivity to feeling full)
Lower glucagon secretion (meaning the liver releases less glucose into the blood)
Stimulate insulin secretion and production (to process the food that is causing the stomach to feel full)
Decrease beta cell apoptosis (this is the fancy name for programmed cell death. Cells in the body are given a countdown clock. When that clock reaches zero the cell destroys itself. Incretin mimetics tinker with that clock to extend the life of beta cells)
Offer protection from heart disease
Lower blood pressure
I should note that while these drugs do stimulate insulin production like, say, sulfonylureas unlike ‘sulfs’ the evidence shows this does not accelerate the demise of the diabetic’s honeymoon. My guess is the suppression of apoptosis counters the effect. Another possible explanation is, because the insulin is getting triggered earlier and harder, the blood sugars are not getting the opportunity to rise as high. Dr. Bernstein talks about how it takes more insulin to bring blood sugar down by a fixed amount when it is at a high level than when it is lower. So, the mimetics get the insulin out early when it is more effective and therefore the net production for a given meal is less.
Types of Incretin Mimetics
There are three types of incretin mimetics. These are:
Amylin analogs: To my knowledge the only one on the market is Symlin (pramlintide). This is available by injection. I have not tried this one. EASD 2020 had a presentation on using it in a dual-hormone therapy (pumps with insulin and pramlintide) but the benefit of the additional pramlintide, in this context, was minimal.
GLP-1 agonists (mentioned above): This is mentioned above with the associated effects. Until recently this was also only available via injection. Recently an oral version has been released (more of that later). I am currently on a once-weekly injection of this drug. Here are the six GLP-1s on the market at the time of writing.
DPP-4 inhibitors: DPP-4s destroy GLP-1s in the body so this drug opposes that destruction and therefore, in principle, eliminates the need to inject GLP-1s. DPP-4s are available in an oral form and I took these for at least six months before moving to the GLP-1 agonists.
Contraindications and Side Effects
I will list some of the more common side effects but this list, like for most drugs, is far from exhaustive so discuss your specific history with your health care team to get a full picture of how well these drugs might suit your circumstances and complication risk profile.
Firstly, incretin mimetics slow stomach emptying so this can make gastroparesis (a condition not uncommon in Type 1s) worse. This slowing can also induce nausea. People on social media will be more than willing to tell you how incretin mimetics made them violently ill. For me, I feel mild nausea a day after injecting the GLP-1 agonist (akin to very mild car sickness) but felt no such effect when taking the DPP-4 inhibitors.
One side effect that was actually beneficial for me was constipation. As I also take Metformin, I have found the GLP-1 agonist and Metformin balance themselves out nicely giving me a lot of freedom I did not have when taking Metformin with the DPP-4 inhibitor. It is different for everyone though as GLP-1 agonists are also reported to have diarrhoea as a side effect.
One of the more serious side effects is the risk of reversible pancreatitis. I am informed pancreatitis is one of the most painful things you can experience and is usually characterized as a sudden, severe abdominal pain extending through the body from the back to the front. As the name suggests, stopping the medication reverses the condition.
LADA-Related Studies on DPP-4 Inhibitors
Given I am a Type 1 LADA trying to preserve my beta cells as long as possible and avoid the financial and mental stresses of insulin management, many of the studies I have earmarked, up to EASD 2020, are focussed on blood glucose management and beta cells preservation. Here are a few:
Tina Vilsbøll presented on Semaglutide, a new GLP-1 which can be taken as a once-weekly injection or as a once-daily oral pill, both being equally effective.
Juris Meier went into more detail as to the efficacy of the oral version, compared to other GLP-1 agonists, in Type 2s, showing it was more effective at reducing HbA1c.
The oral version of Semaglutide also compared favourably to the DPP-4 inhibitor Sitagliptin at doses above 3mg for Hba1c reduction and weight loss.
The downside was an increase in adverse reactions at the higher doses, compared to Sitagliptin.
So, if you are considering swapping out Sitagliptin for Semaglutide, understand that for a superior effect on HbA1c and weight loss you are also at a greater risk of those adverse effects.
Alice Cheng showed how the injectable version compared favorably to both DPP-4 inhibitors and other GLP-1 agonists for reducing HbA1c and weight loss.
For adverse effects, Semaglutide was comparable to other GLP-1 agonists.
My Experiences With DPP-4 Inhibitors and GLP-1 Agonists
As mentioned, I have used both in an attempt to preserve my beta cell mass. For me, the DPP-4 Inhibitor had no side effects or, at least, any side effects were washed out by the ones from Metformin. The DPP-4 Inhibitor did nothing for me in terms of HbA1c reduction (although it is already in the mid-5% for this is not surprising), satiety, or weight reduction.
My experience with the injected GLP-1 agonists has been very different. Given the trouble I was having with Metformin, I halved that dosage but found the two drugs balanced each other nicely, as touched on before. I have lost five kilograms over a couple of months (I have it to lose) which I attribute to my significantly reduced appetite and improved sense of satiety. It is only now I realise it has been years since I actually felt full eating food and the GLP-1 agonist has given this back to me.
tl;dr
While traditionally considered a ‘Type 2 drug’, there can be benefits in incretin mimetics for all Types of diabetics. For all Types we have the general benefits of:
Lowering blood pressure
Lowering the risk of cardiovascular disease
A sense of feeling ‘full’ which may be lacking in diabetics
A lowering of HbA1c
Weight loss (especially for the GLP-1 agonists)
For Type 2s, the benefits also include the lowering of blood sugar levels through the increased release of insulin after meals and supressed release of glucose from the liver.
For Type 1 LADAs there is growing evidence that incretin mimetics can help preserve beta cell function and prolong the honeymoon. Certainly the latest guidelines for managing LADA fully embrace incretin mimetics even if, formally, they are not approved for use in Type 1s.
In terms of side effects, incretin mimetics can cause stomach issues and nausea but, generally, these are mild and much less pronounced in DPP-4 inhibitors.
New innovations in incretin mimetics include:
An improved once-weekly GLP-1 injection and
An oral GLP-1 taken once-daily
While superior to similar products on the market in terms of their effects on HbA1c and weight loss, it comes at the cost of increased risk of side effects such as nausea, diarrhoea, and vomiting.
One of the presentations at EASD 2020 looked at whether adopting a low carb diet provided benefit to hybrid loopers. Given I know of low carb loopers that no longer declare meals I expected the answer to be “yes”, just like the cycling presentation, but I believe there is still value in looking at how the study was put together and what it specifically found. As usual we have tl;dr at the bottom if you want to cut to the chase.
Essentially, she looked at the data across 30 days of, based on the picture below, a Medtronic looping pump. Carbohydrate intake was taken from the participant declarations to the pump. In total there were 36 participants involved in the study.
To work out if lower carbohydrate intake affected Time in Range, rather than compare participants against each other, they took an average carb intake for each participant over 30 days and then measured their intake relative to this for each day.
Why did they not just compare the low carb folk to the high carb folk? I am not sure but perhaps this would introduce confounding factors e.g. perhaps low carb folk are skinnier than high carb folk and it could be the difference in weight causing the better control. By measuring to the individual, these issues are eliminated.
UPDATE: Dr. Lehmann was kind enough to respond to this question saying “We chose a relative approach to account for the intra- and interindividual variability in CHO intake and the confounding factors including weight (a tall, heavy man eats more carbs in grams per day than a small, lightweight woman). We could not categorize our population into low carb folk and high carb folk since there was a high variability in CHO intake between each day (some people had an individual range between 70 to 240 g carbs per day).”
Results
There was not too much of a surprise in the results.
The low carbohydrate days had the highest Time in Range (blue) at around 80% but even the high carbohydrate days were not bad at around 70%.
For Time Above Range (green) the low carbohydrate days were up there for about 15% of the time and this went up to around 25% of the time for the high carbohydrate group.
No statistically significant conclusions could be drawn for Time Below Range.
What is interesting to me is that, even with the high carbohydrate days, the percentages were still within the recommended guidelines for diabetics.
Limitations
Limitations acknowledged by the presented included:
All participants were recruited from the same medical centre which means selection bias is possible
The study was over a limited time period and longer time studies were recommended
Carbohydrate quality and other macronutrients were not considered in the analysis
Carbohydrate values were the estimated declarations of the participants with no validation of accuracy
The participants were mostly male Caucasians
tl;dr
There is evidence that, even with a hybrid looping system, lower carbohydrate intake results in a better Time in Range (TIR) and a reduced Time Above Range (TAR). However, looking at the results, the TIR and TAR values were still within the recommended guidelines for the days when participants ate higher levels of carbohydrates than usual. In other words, if you are using a hybrid loop and you have the occasional carb-loaded day, it seems the looping system is sufficiently robust to keep it from being a complete disaster from a blood glucose perspective.
The exciting aspect of this for me is, assuming the TIR guidelines are accurate in minimising the risk of long term complications, as looping technology becomes more widespread, we should see a measurable reduction in such complications and a lot less mental stress for diabetics with automation making care management easier.
I posted this Tweet based on one of the presentations at EASD 2020.
It is worth going through some of the details because it seems incredible that as little as one hour a week of cycling really reduces all-cause mortality by more than 20% in diabetics. It is also a great study to see the limitations of research and statistics. As usual, if you want to cut to the chase, head on over to the tl;dr section.
They took the data and filtered it to people with diabetes at the start of the EPIC study and for which relevant data had been recorded. This brought the number of participants down to 7,513 of which 63% were confirmed to have diabetes with the others being self-reported. A second cohort was considered at the second examination point to see how a change in cycling habits changed mortality rates. This second group comprised of 5,506 people.
The forms of mortality looked at were all-cause mortality (dying for any reason) and cardiovascular mortality (presumably because it is a common killer and one which is a higher risk in diabetics).
Looking at other factors of the diabetes group, it was seen that the participants were mostly middle-aged and overweight, and most did no cycling at all.
Despite the shrinking cohort numbers, they still obtained the graph I tweeted.
The bars on the four points can be thought of as the error in the value (with this level of error we are 95% confident the value is correct, equivalent to a p-value of 0.05 and is considered statistically significant). So, for example, if I do between 1 and 59 minutes of cycling in a week, my risk of all-cause mortality (ACM) drops to somewhere between 60% and 95% of the baseline risk.
The presenter claimed the covariates (other factors we know about the people) were statistically removed from affecting the numbers attributed to the cycling. So, for example, given women generally live longer than men, this could have an effect on the results so this was accounted for when looking at the results.
The presenter further claimed that there was a j-curve relationship with the data i.e. the benefits are reduced at the 300+ minute mark. To explore this, rather than bucket the cycling times into four groups, they looked at the cycling times as they were i.e. a continuous spread of values between zero and nine hours of cycling per week.
While there is a curve with a maximum benefit around 4-5 hours, there is a fairly wide error margin (UCL = Upper confidence level, LCL = Lower confidence level i.e. the upper and lower values with a 95% confidence of being right). In other words, while the curve seems to head up after five hours, the error means it could just as easily be heading down to between 0.6 to 0.7. Similar results were obtained when cardiovascular mortality was specifically examined.
Next was the review of the effect of changing cycling habits.
The takeaway here is starting to cycle yields at least a 10% reduction in all-cause mortality while maintaining a cycling habit reduces risk by between 20% and close to 50%. For cardiovascular risk, maintaining a cycling habit had slightly better results with the risk reducing by 30-60%.
Recognised Limitations
The presentation did talk at some of the limitations:
Confounding factors: As mentioned they looked at the influence other factors, such as age and smoking had but claim their results still stand
Medication: There was no information on the medications being used by the diabetics and the effect they would have on mortality
Underlying disease leading to less exercise and more mortality (reverse causation): As with the confounding factors, they looked at the potential influence and ruled it out as affecting the results
There was no distinction in EPIC between Type 1 and Type 2 diabetes so no conclusions between the Types could be made but it is assumed the distribution in the study is close to the general population and, therefore 80-90% of the cohort was Type 2
tl;dr
For middle-aged Western European diabetics, the study showed:
Cycling reduces all-cause mortality and cardiovascular mortality
The level of benefit is harder to quantify but, the best benefit was shown to be at around 4-5 hours per week of cycling where the reduction in risk was roughly between 20-40%. Additional cycling may be of benefit but the error margins were too high to confirm this one way or the other
Starting a cycling habit or maintaining one shows benefit with the most benefit being for someone who maintain a cycling habit with the reduction in risk being between 20-50% for all-cause mortality and 30-60% for cardiovascular mortality
There was no distinction of Type so it is best to assume these results apply most strongly to Type 2 but I can see no reason why it would be different to Type 1s except for the additional risk of hypo if insulin dependent
With more looping systems coming to the market from the major manufacturers, there is more research being done into their efficacy, which is great. The focus of a few presentations at EASD 2020 was the benefit of looping systems for Type 1 children and adolescents. What was very interesting was they all came to similar conclusions. This blog will give a summary of those conclusions. Tl;dr available at the bottom for those who do not want to read the details of the individual studies.
Presentation 1: Glycemic Control Improves Over 4 Month Use of Closed Loop Insulin Delivery in School-Age Children with Type 1 Diabetes
For this study the t:slim X2 with Control-IQ was used over 16 weeks. 100 children participated between the ages of 6-13 and none had used looping before.
The results were all positive. For Time in Range, there was an improvement over the control group. The control group was one quarter of the participants using a pump with no looping.
From other presentations at EASD 2020, there is evidence to back up the assertion that a Time in Range (70-180mg/dL aka 3.9-10 mmol/L) of 70% or better significantly reduces the risk of long-term complications. I will write about this evidence in another blog but, for now, you will have to take my word for it. As you can see above, the looping nudged the participants into this 70% mark for the duration of the study. The more interesting results were in the Time of Day analysis.
This is a study of Time in Range over the day. Comparing the Control line to the Closed Loop line we see the big difference between midnight and 9am. While there is some benefit over the rest of the day, the loop comes into its own in those early hours, presumably dealing with dawn phenomenon, although the poster did not go into details. We also see a significant narrowing of the variability during the same period as well.
Looking at the baseline measurements, at the start of the experiment compared to 16 weeks later for the control group (SAP) and the closed loop group (CLC) we see significant benefits for looping.
Time in Range (TIR) more than tripled and Time Below Range (TBR) also improved with a similar tripling.
Presentation 2: Nine Months Experience on Hybrid Closed Loop System in Children and Adolescents Previously Treated with Multiple Daily Injections
This study used the Medtronic MiniMed 670G over 9 months. The 30 participants were 7-18 years of age. There was no attrition during the study (no DKA, severe hypoglycemia, and no hospital admission) and all participants continued to use the pump (no dropouts).
As with the previous study we had a breakdown, based on the time of day.
A lot of numbers here, and I really need to write a blog on how to read scientific technobabble but, the p-values need to be less than 0.05 to make the HCL value to the left of it statistically significant. So, in other words, all the results were interesting, except for the change in weight.
Interpreting the wall of numbers, SG = Sensor Glucose Average, HbA1c is self-explanatory and TDD = Total Daily Dose (of insulin). As we can see the HbA1c dropped from 8.2 to around 6.8 and remained stable over the 9 months. From my previous analysis, we know keeping the HbA1c below 7% significantly reduces the risk of complications.
While, in the previous study, the benefits were mostly during the night, this was not the case in this study with a drop of around 50 mg/dl (2.8 mmol/L) in the sensor glucose average across the day.
The study also looked at the time in range covering the same ranges as the previous study.
Comparing multiple daily injections (MDI) and Manual Mode i.e. no looping to the looping numbers we see a similar result to the previous study with looping nudging the participants into the all-important 70%+ zone for Time in Range. We also see no increase in Time Below Range with the ground gained coming from the high glucose levels.
Presentation 3: Glycemic Outcomes and the Importance of Active Insulin Time in the Pivotal Trial of the Minimed Advanced Hybrid Closed-Loop System
As the title suggests, the study used the Medtronic MiniMed 780G, the model up from the 670G. Arguably the biggest difference between the two models is you can set the 780G to the more aggressive target of 5.6 mmol/L (100 mg/dL) compared to 6.7 mmol/L (120 mg/dL) for the 670G. While details of the participants were limited, there were 39 adolescents in the study which went over three months.
Again, as with the previous two studies, time of day numbers were given.
Comparing the Run-in (baseline) to the Study numbers we see that, as with the first study, there was significant improvement in the night-time numbers and a narrowing or variability, especially in the 5.6 mmol/L graph. While the first study looked at Time in Range, this one looked at the Sensor Glucose Average and while there were no ‘unicorns’, a drop of 30 mg/dL (1.7 mmol/L) is nothing to complain about.
For Time in Range, we saw similar results to the previous two studies.
The Time in Range nudged into the 70%+ value with most of the benefit coming from the above-range time.
Presentation 4: First Home Evaluation of the Omnipod 5 Automated Insulin Delivery System, Powered by Horizon in Children with Type 1 Diabetes
This case, as stated it is the Omnipod 5. The 18 children in the study were between the ages of 6 and 14 and participated over three months. There was a slight difference in this study in that different targets for the looping system were also compared.
Again, we have Time of Day numbers with, in this case, different target levels for the looping system.
The red line is for non-looping and the blue line is looping. In this case we do not see a significant shift in the line but we do see a narrowing of the fluctuation in numbers, again, in the first third of the day supporting the notion that looping systems are good for overnight control.
For the table of numbers, anything with an asterisk is statistically significant. From this we can see that Time in Range went from 51% up to over 60% for the lower targets. This is less than the 70% seen with other systems but an improvement regardless (and the presenter was very keen to disclose that the study was done over Christmas). Time Below Range and Time Above Range were both reduced for the lower target settings.
For Time in Range,
we see that 10 out of the 18 children managed to achieve a Time in Range of greater than 70%, compared to 2 out of 18 for the non-loopers.
Also, when the more aggressive target of 6.1 mmol/L (110 mg/dL) was employed, we see the lowering of the overnight blood glucose levels we saw elsewhere and a reduction in high numbers.
tl;dr
Children can benefit greatly from looping systems compared to multiple daily injection or non-looping CGM/Pump setups. Key benefits include:
An overall improved Time in Range (70-180 mg/dL aka 3.9-10 mmol/L) with values generally over 70% which is the critical mark to significantly reduce the risk of long term complications. The more aggressive targets (lower than 7 mmol/L aka 126 mg/dL) were the most successful at getting 70%+. Time Below Range was not always affected compared to Time Above Range which was consistently significantly reduced.
Overnight blood glucose values were especially improved with looping both in terms of the average sensor glucose and time in range. The variability of the numbers was also improved with looping.
The results were largely independent of the looping system employed; the benefits are down to the overall technology, not any one company’s version of it.
As a dedoc Voice I had the privilege of attending EASD 2020 (European Association for the Study of Diabetes annual conference) from 21-25 September. I will be doing detailed posts of some of the announcements from the conference over the next few weeks with this being the first.
For those that use insulin pumps, one of the announcements was the development of a 7-day infusion set by Medtronic, instead of the usual 2-3 day sets on the market today.
They looked at the various aspects of delivery which could affect performance and found that insulin stability and preservative concentration, which dropped over time, were critical factors leading to hyperglycaemia and the need to remove the infusion set.
To address the stability factor, they changed the reservoir cap to alter the flow (fluid dynamics can play havoc with insulin stability, apparently), changed the tubing material to help preserve the preservative, and improved the patch adhesive so it would last longer.
Once the new design was created, 20 participants wore the infusion sets for a month (four in total for each person).
The graph in the middle is the key piece of information here.
The y-axis is survival percentage (how many infusion set were still working) and the x-axis is the days of wear. The green line is the usual 2-3 day infusion set and the purple line is the new infusion set.
To reach the point where 20% of the infusion sets had failed (80% survival) took the new infusion set seven days, compared to, unsurprisingly, 2-3 days for the 2-3 day infusion sets.
One of the claimed incidental benefits is the saving of insulin left in the tubing and reservoir (as well as insulin wasted in priming) which, according to Medtronic, could account for as many as 5-10 vials of insulin per annum. With insulin pricing being what it is in the US, it is easy to see how this is compelling. Given the system also needs a special reservoir, I am hoping the costs to the tech do not outweigh the savings in insulin.
It is often said that every diabetic’s journey is unique and it is true. While many of us have similar, isolated experiences, no two journeys are the same.
Paradoxically, the diabetic community can be quite tribal with groups excluding those who diverge from the ‘norm’. As a Type 1 LADA I see this a lot. As an example, when it is said ALL Type 1s use insulin and I have the temerity to point out I do not, I am dismissed as not being a ‘true’ Type 1. Only when my honeymoon ends, it seems, will the LADA be accepted into the tribe as a ‘real’ Type 1. I see little value in this dismissal of diversity.
Is LADA Really Type 1 Though?
If we define the Types by etiology (cause) then LADA falls squarely in the Type 1 camp. Both are auto-immune and, at a point in time, there is literally no test that can be performed to distinguish a LADA from a honeymooning Type 1. The only difference between someone with LADA and someone with ‘classic’ Type 1 is the rate of progress of the disease. While the honeymoon period of a ‘typical’ Type 1 is, at most, a few weeks, a LADA can be insulin independent for up to decade. It is a long time to wait for acceptance.
The other option would be to define the Types phenotypically (how it presents) but it is hard to derive an adequate criteria to separate Types 1, 2, and LADA (or any other Type) on this basis. As alluded to above, the dependence on insulin is often suggested as the distinguishing factor but there are many Type 2s/MODYs/Gestational diabetics who use insulin and all LADAs eventually progress to insulin dependence meaning, by this paradigm, LADAs gradually transition from one Type to another which, in itself, is problematic.
Diabetes UK unambiguously consider LADA as a form of Type 1. Similarly JDRF Australia say “A slower onset form of Type 1 diabetes, known as Late-onset Autoimmune Diabetes of Adulthood (LADA) generally presents with milder symptoms of hyperglycaemia, and insulin therapy may not be immediately required at the time of diagnosis.”
The Struggle for Recognition for all Diabetics
The concept of the only ‘true’ Type 1 being one which uses insulin is a fallacy and is akin to the ‘No True Scotsman’ fallacy.
Person A: “No Scotsman puts sugar on his porridge.” Person B: “But my uncle Angus is a Scotsman and he puts sugar on his porridge.” Person A: “But no true Scotsman puts sugar on his porridge.”
This struggle is not unique to LADAs though; all Types have their frustrations when it comes to recognition. This became clear to me at ‘docday’. Docday was a gathering of the dedoc Voices with presentations by some of the Voices on their advocacy work. Ken Tait presented on his frustrations as an insulin dependent Type 2. It was Ken’s talk which inspired me to write this post.
So often Type 2s are assumed to have brought the disease on themselves and that it is a ‘lifestyle’ disease. It is also often assumed that if they changed their ‘lifestyle’ the disease would go away. While there are lifestyle ‘risk factors’, there is no known cause of Type 2 diabetes and many Type 2s simply do not fall into any of these ‘lifestyle factors’. No amount of sugar eating gives you diabetes, there are plenty of overweight people who are not diabetic (and skinny ones who are), and while Type 2 can go into remission through a low calorie diet or gastric surgery, this is only effective in something like 70% of cases. Just as it would be foolish to assume someone from Australia has blonde hair and blue eyes, it is equally as foolish to assume the journey of every Type 2 is the same.
For Type 1s, the struggle is often to be seen as separate from Type 2s. While the cause of Type 2 is unknown, Type 1 is auto-immune (the reason for the immune system going haywire is still unknown but I digress). Type 2s represent around 90% of all diabetics so a lot of research and a lot of articles in the popular press focus on Type 2s almost exclusively. Frequently Type 1s are told that they can put their disease into remission. While this is only partially true for Type 2s, it is completely untrue for Type 1s. Short of a pancreas/islet transplant and immune suppression drugs, there is no remission for Type 1 diabetes.
As mentioned, for LADAs we are rarely seen as ‘true’ Type 1s. We are either labelled as ‘self-loathing Type 2s’ or ‘honeymooning Type 1s’. With a honeymoon that can last up to a decade, for me, dismissing their voice as meaningless is short changing 10% of the Type 1 community. If we can accept other diseases as having stages/progression, why is this so hard for Type 1 diabetes?
Clinical Consequence
Where the issue becomes a little more serious is in the consideration of treatment for the various Types. According to the NDSS (registration database for all diabetics in Australia) there are literally no Type 1 diabetics in Australia who are insulin independent. Given I am one, how is this possible? Like with the registration forms of many diabetes associations, it is simply assumed that if you are Type 1, you use insulin.
To make matters worse, subsidies for medication and equipment is often separated down Type lines. The medications I use to help preserve my beta cells and maintain my insulin independent honeymoon are considered ‘Type 2’ medications so I receive no subsidy for these and obtain them through a ‘private prescription’ at full price. While proven to be effective and safe, this means there are many LADAs who, for no good reason, cannot access the medications they need to remain healthy and live to their full potential.
The notion of Type 1 and Type 2 medications is problematic, not just for LADAs. The same drugs which help me, can help insulin dependent Type 1s with insulin resistance (yes, some Type 1s are insulin resistant) and satiety (one of the hormones which make you feel ‘full’ after a meal is missing in Type 1s i.e. Amylin). This notion of feeling full can also prevent Type 1s over-eating, reduce the amounts of insulin they need, and ameliorate weight gain which is often associated with insulin when it is first used.
Strength in Diversity
Accepting that not all diabetics are the same, even within a Type, does not diminish the journey of any one individual. What it does do is give a voice to all and give other diabetics and health care professionals a more nuanced understanding of this heterogeneous disease. With diversity of experience comes diversity of thought and better solutions to the problems we all face. Every diabetic’s journey is different but we can still walk together and exclusion simply means we walk alone.
It is always humbling when strangers puts their faith in you. This happened to me this week when I was accepted as a dedoc Voice. Dedoc is an international network of diabetes advocates. What is more, it is Type agnostic: all are welcome.
Dedoc also paid for me to virtually attend EASD 2020 (the annual meeting of the European Association for the Study of Diabetes). Usually such conferences are exclusively for medical professionals but dedoc negotiated to allow their ‘Voices’ to attend with a view of socialising the research for a wider audience. Scientists are not always the best at communicating their discoveries so I believe this can only be a good thing.
There was way too much for me to communicate in one blog post so I will be posting a few. For some of the ‘soundbyte’ discoveries, check out my Twitter feed.
I mentioned in the article comparing ketosis and diabetic ketoacidosis (DKA) that, in DKA (Diabetic Ketoacidosis), the liver floods the bloodstream with fuels, such as ketones which makes the blood acidic. However, the usual way a diabetic tests for DKA is to see if there are ketones in the blood. The problem with this approach is ketosis also releases ketones into the blood but ketosis is not dangerous. If we find our blood has ketones how do we know if we are in ketosis or DKA?
As usual, for the quick answer, head over to tl;dr.
The other measure often applied is to test the blood glucose level. With the liver flooding the blood with fuels during DKA, including glucose, it makes sense that the blood glucose levels will rapidly rise. This is usually true, unless the liver’s glucose stores are depleted. This situation leads to a condition called eDKA (Euglycemic DKA). This is a big problem for diabetics who follow a low carbohydrate diet as this can put the person into ketosis but can also deplete the glycogen stores of the body which means, if they do head towards DKA, there may not be a corresponding rise in blood glucose levels.
How is DKA Medically Defined?
DKA is not defined by ketone levels, even though this is often the measure used by diabetics to check. It is actually defined by the pH (acidity) of the blood and the amount of bicarbonate in the blood.
The normal pH of blood is between 7.35 and 7.45 and the body goes to great lengths to keep it in this range. In DKA this goes below 7.3. If only there was a way for diabetics to directly check what the pH of their blood was, this would remove the ketone-confusion. I am happy to say there is a way to check.
The Magic Meter
It took some finding but I have found a pH meter which can measure pH levels to one hundredth of a pH (pH is unitless). The meter is the LAQUAtwin-pH-22 by Horiba Scientific.
The tapered end has a flap which lifts up and a liquid sample is placed on the ISFET sensor underneath (0.1 mL or more). This meter costs around $200 so it is not cheap but cheaper alternatives, such as testing strips and immersive meters, simply do not have the accuracy needed.
To set up the meter ready for testing, you need to calibrate it with the provided solutions. Once this is done (and if you follow the instructions it really is quite easy) you are ready for your blood.
Getting a Blood Sample
It turns out getting blood out of the human body was a lot harder than I realized. I initially tried finger pricking but generating 0.1 mL of blood this way proved painful and futile as I struggled to get a reliable reading. In the end I used butterfly needles and vacutainers ordered online. This combo is what the blood collection folk use to collect blood for analysis.
To get the blood, you remove the needle cover, put the needle into your arm and then push the green end of the butterfly needle through the hole in the top of the vacutainer tube. For tips on technique, I highly recommend searching YouTube (this is what I did).
The end result was a vial full of blood.
I did also consider using syringes, given diabetics (or, at least, insulin dependent ones) have easy access to them but this way looked the simplest given I only had one free hand during the process.
For the curious, the vacutainer tube has a vacuum inside and so when the green end of the butterfly needle is pushed into the tube, the vacuum draws the blood out of the arm.
If you intend to try this at home, one other tip is to use the sample quickly. In my case, if I left it too long, the blood in the tube began clotting so get the blood into the meter quickly before coagulation.
The Result
The meter lived up to its promise and I got a reading of 7.48. This is a little on the alkaline side of the normal blood range but may have been a minor calibration error. Even accounting for the calibration, this is much higher than the 7.3 ‘danger zone’ so no DKA for me today.
tl;dr
Using the LAQUAtwin-pH-22 you can measure the pH of a liquid down to a sample size of 0.1mL and to an accuracy which is meaningful for checking for DKA. Using a butterfly needle and vacutainer I obtained online, I extracted blood from my arm and, using the meter showed a sensible result, consistent with someone who is not in DKA.
This opens up a new way for diabetics to check whether they are in DKA and is also a way aligned to the actual medical definition of DKA.
