If you have seen the Glucology or Frio insulin pouches you will already understand the concept of an evaporative cooling insulin pouch. They work really well but the downside is the price. For a two-pen pouch you are looking at around US$30. In this blog I will show you how to make your own for less than US$5 (if you have a sewing machine) or, if that is too much hassle, you can buy a ready-made one from my Etsy shop for around US$20 including shipping anywhere in the world or AU$25 including shipping within Australia. For every one purchased, I will be supplying an identical one to charities around the world who are supplying insulin in developing countries where refrigeration is unreliable.
How Do They Work?
Evaporative cooling pouches work off of the idea that if water is evaporating from something, that something gets cooler. This is how humans cool themselves i.e. sweating. The key technology in the cooling pouches that allow them to remain cool for well over 24 hours are the little beads inside the pouch. These hydrophilic beads (a big word meaning they hold onto water) are the same ones you might know as Orbeez or those squishy spheres you see flowers in sometimes.
Same technology, different application. While the beads love holding onto water, it still evaporates and, while it does, they and anything they are surrounding feels cool to the touch. Embed them in material and that material, and anything it is surrounding also becomes cool.
DIY Step One: The Materials
Firstly, you will need two cooling scarves. They look like this:
You can buy these from Amazon but this will set you back about US$4 each before postage. Go to Ebay and select the international option and, while it might take a little longer to arrive, you can pick them up for around US$1 each.
You will also need a sewing machine or someone with the time to hand stitch them.
DIY Step Two: Stitching
Lay one on top of the other. For symmetry, I have put the seam of one (the white stitching on the edge) to the non-seam side of the other.
Then you will want to do a straight stitch along the edges so the two pieces form a long tube.
The dark line at the bottom on the scarf in the above picture is the straight stitch you need to do on the top and bottom. There is one at the top in the image as well but, because I was using a light brown thread in my bobbin, it is a little tricky to see. While I went a little over in the above picture, you want to go up to the white stitching which runs up and down on the left hand side in the above picture. This gives you the ties on the end of the scarf to secure the pouch, once finished.
DIY Step Three: Inverting
Arguably the fiddly bit, you now push the tube inside of itself to invert it and hide the stitching. I found the best way was to pull it out, rather than trying to push it through with a stick. In the end you will have an inverted tube which looks something like this.
You are now the proud owner of a cooling insulin pouch. Well done!
How To Use Your Pouch
Before use you will need to soak your pouch. I recommend no more than five minutes. Any more than this and the beads will absorb too much water and make it too hard to insert the pens/cartridges/vials. There is probably a risk of splitting the stitches as well.
Once soaked, dry it off with a dishcloth or towel. The material does not have to be wet for it to be effective. It is the beads inside which do the work.
Then, you can insert your hardware.
In the above example, I have my basal and bolus insulin pens and a MedAngel (a Bluetooth thermometer which connects to your phone).
Once inside, you can use the those end ties to keep it all together.
Does It Really Work?
While I am still insulin independent, I always carry insulin whenever I travel overseas for work. Just before the world locked down, I took my pouch to Singapore. Even with Singapore’s humidity, the pouch, within my backpack, was effective at keeping my insulin cool as I walked, in the direct sun, to the office each morning. Tests I have conducted in more controlled conditions show the pouch is as effective as its commercial counterparts.
Here you see three temperature gauges measuring (in Celsius) the temperature in my house (the middle one), the temperature of a commercial pouch (on the left) and my one (on the right). Even inside, both pouches shaved off 2-3 degrees Celsius (4-6 degrees Fahrenheit). The effect becomes even stronger in direct sunlight where I have seen temperature differences of 10 degrees Celsius (20 degrees Fahrenheit) between the ambient temperature and the pouch.
“Recharging”, Storage and Care
The pouch will continue to keep the contents cool for over 24 hours. When you want to “recharge” it, immerse it in water for no more than two minutes. Again, if you over-immerse, you risk expanding the beads too much and making it impossible to insert your pens etc. or breaking the stitching.
If you are not planning on using your pouch for a while, let it completely dry out in the sun or on an air vent and, when completely dry, store it in a cool, dry place.
Let me cut to the chase on this: if you have been recently diagnosed with diabetes and you maintain good glucose control, based on this evidence, there is NO reason to expect you will not live a long and healthy life
This may sound wrong because it is easy, from a statistical point of view, to work out the difference in life expectancy between people with diabetics and non-diabetics and show people with diabetes live for a few years less than non-diabetics. Sure enough, Mike Stedman and his colleagues did precisely this for EASD 2020.
What this tells us is that Type 1 males live 7 years less than their non-diabetic counterparts and, for women, it is 8.5 years. For Type 2, the difference is less pronounced being 1.4 years lost for men and 2 years lost for women.
The problem is the data, while accurate, is very misleading. Moreover, it hits social media and diabetes groups, and Type 1s think it is inevitable that they will live shorter lives than their friends and families. The stresses of diabetes are plentiful without this kind of burden being added on top.
The fact of the matter is this graph says absolutely nothing about the life expectancy of an individual. Rolled into that average life expectancy are hypo deaths, DKA deaths, and things like heart complications. We know higher HbA1c levels increase the risk of cardiovascular (heart) disease so, for someone with good glucose control, in my opinion, the above graph is meaningless. It also says nothing about age of onset. Someone who was diagnosed 50 years ago (included in the above numbers) had to manage their diabetes with urine, thick needles, and a lot of luck. Someone diagnosed today has much more effective tools at their disposal.
In my opinion, it would be much smarter to look how life expectancy has improved over the years and, where possible, see if it is catching up with the non-diabetic population. This is what I want to write about today. As usual, you can skip to the tl;dr for the summary, otherwise let us go on a journey of optimism, rather than of pessimism. Firstly though, what is up with that gender imbalance in life expectancy?
Why Do Females with Diabetes Have a Shorter Average Life Expectancy?
Again, let me repeat the disclaimer: Average life expectancy says nothing about the life of an individual. A woman with good glucose control has, in my opinion, no reason to expect any shorter life than her non-diabetic peers.
To the question at hand though, another presentation at EASD 2020 looked at this in some detail. Juergen Harreiter presented “What you need to consider for individualised gender-sensitive care”. This looked at the gender differences in diabetes risk and treatments.
Sure enough females with diabetes are at a higher risk of coronary heart disease and stroke than their male counterparts.
and a 40% greater risk of all-cause mortality.
Specific reasons, backed up by data, were light on the ground except for this cited study.
In the above graphs, we have four drugs commonly used to reduce the risk of heart disease. For three of them (statins, RAAS blockades, and beta-blockers) women were routinely under-treated. With males receiving more aggressive treatment, it is not surprising they live longer.
So What About Life Expectancy Improvements over Time?
There is overwhelming evidence that life expectancy is improving every year for people wit diabetes. While the cause of the improvement is not always clear, I suspect it has to do with improvements in management technology (insulin improvements, pumping, glucometers, looping etc.) and possibly improved knowledge and education about the disease. Let us go through the papers I have found.
Life expectancy and survival analysis of patients with diabetes compared to the non diabetic population in Bulgaria (2020)
This study looked at data from 2012-2015 using national databases. Even in this short period of time we see improvements across both Type 1 and 2 and across genders.
While the non-diabetic population did not move at all in their life expectancy, Type 1s, Type 2s, Type 1 females, and Type 2 females all gained around an additional year, on average. Moreover, by 2015, the overall life expectancy of people with diabetes was about the same as the non-diabetic population (almost 75 years).
Improvements in the Life Expectancy of Type 1 Diabetes (2012)
In this study the life expectancy of two cohorts was examined: people with diabetes diagnosed between 1950-1964 and between 1965-1980.
Here we see the percentage of survivors after a certain age. The EDC (Pittsburgh Epidemiology of Diabetes Complications) cohort for 1950-1964 are lower on the graph as fewer survived over the years. In comparison, the 1965-1980 cohorts for EDC and ACR (Allegheny County Type 1 Diabetes Registry) had a much higher survival rate again showing the difference just 15 years made in the life expectancy of people with diabetes.
Long-Term Mortality in Nationwide Cohorts of Childhood-Onset Type 1 Diabetes in Japan and Finland (2003)
This study was designed to compare Type 1 diabetics in Japan and Finland but also had information of improved survival rates over the years.
While in Finland, the survival prospects of a Type 1 child remained excellent for the periods 1965-1969 and 1975-1979 with only minor improvements, we see there was a dramatic improvement in survival probability for children diagnosed in the latter period for Japan.
The 30-Year Natural History of Type 1 Diabetes Complications (2006)
This study looked at five 5-year cohorts (1950-1959, 1960-1964, 1965-1969, 1970-1974, and 1975-1980) also from the Pittsburgh Epidemiology of Childhood-Onset Diabetes Complications Study. Not surprisingly, it came to similar conclusions.
We see a clear improvement in survival rates with around 60% of those diagnosed in the 50s and living with the disease for 30 years surviving, compared to around 95% for those diagnosed in the 70s.
All-Cause Mortality Trends in a Large Population-Based Cohort With Long-Standing Childhood-Onset Type 1 Diabetes (2010)
This study split the previously mentioned ACR study into three diagnosis cohorts (1965-1969, 1970-1974, and 1975-1979) to compare survival rates.
As we have seen in the other studies, there was a dramatic improvement in survival rates between someone diagnosed in the late 60s compared to the 70s.
A Word of Warning
In the above studies we have looked at people with diabetes in Bulgaria, USA, Japan, and Finland and, as mentioned, it is likely the improvements are due to improvements in medical technology and education. For countries where these improvements are not available, there is no reason to expect the same gains. For places in the world where insulin is not readily available, there is no reason why life expectancy should be much better for a person with diabetes than it was before the discovery of insulin 100 years ago. These studies show there are compelling reasons for insulin to be available to all who need it, across the world. We are literally robbing people of decades of life if we choose to do nothing.
There is clear evidence that the survival rates for people with diabetes has improved for those diagnosed between 1950 and 2015. Furthermore, in a study from Bulgaria by 2015, the life expectancy of people with diabetes was the same as non-diabetics.
While the specific cause of the improvements was not examined, it is assumed to be a function of improved technology (insulin, pumps, glucometers, looping etc.) and improved understanding and education of the disease.
Therefore, if you have been recently diagnosed with diabetes and you maintain good glucose control, based on this evidence, there is NO reason to expect you will not live a long and healthy life.
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 advances in DNA manipulation, it became possible to manufacture insulin with bacteria. 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 basal insulins, up until now, could work for, at most, 24 hours, this is not the case for Icodec. The claim is this will make basal insulin available to the body over a period of one week.
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 effectivess of Icodec compared to Degludec (Tresiba) and a placebo.
Julio Rosenstock presented on a study with 247 participants comparing daily basal injections with weekly injections in a double-blind, double-dummy test. Icodec did not fare well in terms of hypoglycaemic events.
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:
Excessive alcohol consumption
Excessive use of opiates
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
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.
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 Chen 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.
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.
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%.
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
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
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?
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 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.
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.
I often see people claim on social media that glucose spikes above 110/120/140 mg/dl (roughly 6/7/8 mmol/l) cause damage and diabetics should religiously keep their blood sugars below this level to prevent long term complications.
While the research to verify this assertion could well be done with the wealth of data now captured by continuous glucose monitors (CGMs), to my knowledge, it has not been done. My concern is that being this fixated on your glucose levels would be a great way to drive yourself crazy and a tragedy if it was for no benefit.
So what does science know? In this article I will review the literature as well as show you what to look for in other medical papers. As usual, feel free to go to tl;dr if reading scientific paper summaries is not your thing.
My first source is a recent study from Sweden which suggests there is a ‘goldilocks zone’ for diabetics where the HbA1c is not too high to cause complications and not too low to increase the risk of severe hypoglycemia. Their conclusion is an HbA1c between 6.5% and 6.9% is optimal to avoid these two extremes.
My second list of sources come from Blood Sugar 101. This is a site that claims their cited scientific papers “make a cogent case that post-meal blood sugars of 140 mg/dl … cause both permanent organ damage and the worsening of diabetes.” I am keen to review their papers to see if the papers actually support this position. I have nothing against the site, I barely know it. I chose it simply because it was cited in social media and I assume they have chosen papers to give the most compelling case for their claim.
What To Look For In Medical Papers
When reviewing papers and their findings, I look at two things: their ‘n’ and ‘p’ values. The ‘n’ is the number of people involved in the study (obviously the bigger, the better) and the ‘p’ value which measures statistical significance. The lower the ‘p’ value, the more reliable the conclusions with a value below 0.05 generally considered to be statistically significant.
For example, the Swedish study mentioned above had n=10,398. That is quite a big study. The Results section says the following:
“Mean age of participants was 14.7 years (43.4% female), mean duration of diabetes was 1.3 years, and mean HbA1c level was 8.0% (63.4 mmol/mol). After adjustment for age, sex, duration of diabetes, blood pressure, blood lipid levels, body mass index, and smoking, the odds ratio for mean HbA1c <6.5% (<48 mmol/mol) compared with 6.5-6.9% (48-52 mmol/mol) for any retinopathy (simplex or worse) was 0.77 (95% confidence interval 0.56 to 1.05, P=0.10), for preproliferative diabetic retinopathy or worse was 3.29 (0.99 to 10.96, P=0.05), for proliferative diabetic retinopathy was 2.48 (0.71 to 8.62, P=0.15), for microalbuminuria or worse was 0.98 (0.60 to 1.61, P=0.95), and for macroalbuminuria was 2.47 (0.69 to 8.87, P=0.17). Compared with HbA1c levels 6.5-6.9%, HbA1c levels 7.0-7.4% (53-57 mmol/mol) were associated with an increased risk of any retinopathy (1.31, 1.05 to 1.64, P=0.02) and microalbuminuria (1.55, 1.03 to 2.32, P=0.03). The risk for proliferative retinopathy (5.98, 2.10 to 17.06, P<0.001) and macroalbuminuria (3.43, 1.14 to 10.26, P=0.03) increased at HbA1c levels >8.6% (>70 mmol/mol). The risk for severe hypoglycaemia was increased at mean HbA1c <6.5% compared with 6.5-6.9% (relative risk 1.34, 95% confidence interval 1.09 to 1.64, P=0.005). “
It looks complicated but we can break it down. When it compares the risk of any retinopathy between those with an HbA1c < 6.5% and those with 6.5-6.9% the odds ratio is 0.77 (you are less likely to get retinopathy with the higher HbA1c) BUT the ‘p’ value is 0.10 so it is not statistically significant and we can ignore it. In fact, in comparing the <6.5% group to the 6.5-6.9% group, the only statistically significant result was for preproliferative diabetic retinopathy which was right at the edge of significance (p=0.05).
However, when comparing <6.5% to 7.0-7.4% and >8.6%, across the board, there was a statistically significant increase in risk for all of the examined complications.
Finally, when comparing the risk of severe hypoglycemia between <6.5% and 6.5-6.9% there was a statistically significant increase in risk below 6.5% (34% higher).
The paper’s conclusion is:
“Risk of retinopathy and nephropathy did not differ at HbA1c levels <6.5% but increased for severe hypoglycaemia compared with HbA1c levels 6.5-6.9%. The risk for severe complications mainly occurred at HbA1c levels >8.6%, but for milder complications was increased at HbA1c levels >7.0%”.
This makes sense and I believe the “mainly” is inserted to cover the borderline preproliferative diabetic retinopathy risk increase for the 6.5-6.9% group.
Now let us look at the case for “140 mg/dl does damage” by going through the Blood Sugar 101 sources.
Reviewing the Papers
Some of the Blood Sugar 101 links were broken but here are the ones which actually went somewhere or which I could find by Googling the title.
n=107 of which only 13 had diabetes and 36 had impaired glucose tolerance (IGT) and all had idiopathic (unknown cause) neuropathy.
The paper found people with IGT (defined as having a blood glucose of 140-200 after two hours in an oral glucose tolerance test (OGTT)) had a statistically significant higher change of having neuropathy BUT no such conclusion was made for diabetics. In other words, the low population of the study, combined with the low population of diabetics means this paper offers little value to diabetics and yet I have seen it quoted on a few sites claiming it backs the “over 140 mg/dl does damage” claim. At best, we can say it supports the claim that people who are prediabetic are at a greater risk of neuropathy, but that is about it.
This study mirrored the previous one with n=73. Of these patients, 26 had IGT, and 15 had diabetes. This paper shows that diabetics that have neuropathy have it more severely than those just with IGT. So, in this case, the conclusion is if someone is at 200 after two hours of an OGTT (the definition of ‘frank’ diabetes) if they get neuropathy it will likely be more severe than their prediabetic counterparts.
In this one n=100, all with chronic idiopathic axonal polyneuropathy (CIAP). They were given an OGTT and 62 of them had abnormal results, twice as high as general population groups. Statistical significance was a little light on the ground in this study but it is aligned to the previous two studies’ findings.
This study had n=195 diabetics and n=198 control subjects. It found diabetes was a risk factor for polyneuropathy and, within the diabetic group, age, waist circumference, and peripheral arterial disease were associated with polyneuropathy.
This study tried to keep n=800 critically ill patients patients below 140 mg/dl while in the Intensive Care Unit (ICU) over a period of 11 months and compared them to patients who were not intensively managed. The populations were not all diabetic with the only common factor being admission to ICU.
The following were shown to have a decreased incident rate in the intensively managed patients: poor kidney function (renal insufficiency), blood transfusions, hospital mortality rates, and length of stay in the ICU. Hypoglcemia rates did not significantly change.
This one is in mmol/l but I will convert for the US diabetics. Essentially it showed that when blood glucose goes above 100 mg/dl, the ratio of insulin sensitivity to insulin resistance declined. However, the paper failed to report the level of statistical significance of the results. It did say it used n=388 though of which 250 had IGT or Type 2 diabetes. So, assuming the results were significant, it tells us that either resistance increases or sensitivity decreased as blood glucose levels go up.
This study reviewed the beta cell mass of bodies from 124 autopsies. Of the 124, 91 were obese and 33 were lean. They found that obese patients had roughly a 50% larger beta cell volume (possibly influenced by the younger age at which the obese population died). Of the obese individuals, the Type 2s had a 63% smaller beta cell volume than their non-diabetic obese counterparts.
The rest of the paper talks at the possible mechanisms for this difference is volumes, looking at beta cell replication rates and beta cell death rates.
This is a mice study and, given the number of ‘cures’ for diabetes found for mice, I am a little skeptical to apply the findings to humans. The paper was looking at the survival rate of transplants between mice with insulin treatment to keep their glucose below 150 mg/dl and mice with no such treatment.
The paper found:
“…insulin treatment did not improve the initial preservation of transplanted β-cell mass in the initial days after transplantation. In contrast, increased apoptosis (cell death) and reduced β-cell mass were found in islets exposed to long-term hyperglycemia but not in normoglycemic mice, suggesting that sustained hyperglycemia increased β-cell death in transplanted islets.”
So transplanted beta cells in mice did not appreciate long term exposure to elevated glucose levels.
This study gave n=1062 patients an OGTT and measured their blood glucose after one hour. Those above 155 mg/dl had elevated inflammatory markers and lipid ratios. The author goes on to suggest these increases could be a marker for cardiovascular risk but does not provide evidence linking the markers to heart disease.
This was a link to another Blood Sugar 101 page which had a bunch more links but, given the length of this article already, I am focusing on the ones just on the original page. If enough people call this out, I am happy to review the heart disease one in another article.
Broken link and could not find the source on Google. I did find this summary but without indication of statistical significance it is hard to confirm the findings. Also, the paper focused on pre-diabetes so its relevance to diabetics is limited especially when no blood glucose levels are mentioned. I expect it found conclusions similar to papers (1), (2), (3), and (4).
This paper looked at the data of three populations (n=3162, n=2182, and n=6079). Its conclusion was:
“We saw no evidence of a clear and consistent glycaemic threshold for the presence or incidence of retinopathy across different populations. The current FPG cutoff of 7·0 mmol/l used to diagnose diabetes did not accurately identify people with and without retinopathy.”
In other words, they found that a person’s fasting plasma glucose (FPG) was a poor predictor of retinopathy.
This paper is looking at FPG and HbA1c to see if it is predictive for diabetic retinopathy. With an n=1066 (not all diabetics) they concluded that the greatest increase in prevalence for retinopathy occurred for HbA1c above 5.5% and FPG above 5.8 mmol/L (105mg/dl). It also found that HbA1c was a better predictor than FPG. Here are their curves.
For the HbA1c curve, while the uptick is at 5.5%, we see the dip before this means the prevalence, relative to the baseline prevalence of around 10% only starts inceasing past this above 6%. Similarly, to escape baseline required an FPG above around 6.5 mmol/l
This study combined the results of nine studies to get a whopping n=44,623. They looked at FPG (n=41,411), two-hour OGTT (n=21,344), and HbA1c (n=28,010).
While no ‘p’ values were given, their results concluded that an HbA1c above 6% has an increased prevalence of retinopathy with the threshold for significant risk at above 6.4%. For FPG the threshold was 6.6mmol/l (120 mg/dl). OGTT proved to be a poor predictor.
n=700 with the aim to determine the HbA1c and FPG for predicting retinopathy after 10 years.
Here are the results.
While the paper’s conclusions were thresholds of 108 mg/dL for FPG and from 6.0% for HbA1c, we can see above the prevalence only jumps up significantly after >7.0 mmol/L (126 mg/dl) for FPG and >7.0% for HbA1c.
This was a press release talking about two studies, rather than the studies themselves. Given it is light on details, I am ignoring it for this analysis. It did say this though:
“No one is claiming, based on current evidence, that either fasting glucose or HbA1C is a viable target for therapy of heart failure specifically; that would have to be established in prospective, randomized trials, all three researchers emphasized.”
This paper looked at n=33,293 women and 31,304 men (for a total of n=64,597). Of these, 2,478 people had cancer. The big takeaway of this study was the difference in risk profile between men and women. It found “abnormal glucose metabolism was associated with a statistically significantly increased risk of cancer overall in women but not in men.”
To put it another way: “In men, overall, no statistically significant associations were observed between glucose levels and cancer risk”.
Like study (9), this was a study of cells in a lab, rather than a study of humans. The main conclusion was a fluctuation in glucose levels aligned to the kinds of fluctuations a human body is exposed to through three meals a day and 12 hours of fasting is more damaging than constantly high glucose levels.
n=1871, all diabetics, had their HbA1c measured and then were followed up over a period of 11 years. The groups were split into people with HbA1cs of <6%, 6-7%, 7-8%, and >8%. Groups above <6% had a higher relative risk of chronic kidney disease (CKD).
This means, if there is a threshold for HbA1c, above which CKD begins to increase in risk it probably lies somewhere between 6 and 7%.
This study involves n=19,019 men but the analysis only looked at the non-diabetic ones (n=18,406). The men did a test similar to an OGTT but not quite following modern protocols and if they exceeded 200 mg/dl they were excluded (n=56). Also those with missing data were excluded (n=134) leaving a total of n=18,216.
The study found that for non-diabetics, the risk of stroke mortality increased if the patient’s blood sugar went over 4.6mmol/l (82.8 mg/dl) as part of their pseudo-OGTT. Given my focus is on diabetics, a paper studying non-diabetics is of limited relevance.
Summary Of All The Paper’s Conclusions
So this is what we know from the 21 papers.
If you have impaired glucose tolerance (IGT) you are more likely to get neuropathy
Diabetics who fail an OGTT are at risk of more severe neuropathy than those with just IGT
If you have IGT you are more like to get chronic idiopathic axonal polyneuropathy (CIAP)
Diabetes is a risk factor for polyneuropathy
If you use insulin to keep patients in intensive care under 140 mg/dl, they tend to fare better
Either insulin resistance increases or sensitivity decreases as blood glucose levels go up (this is anecdotally confirmed by Type 1s I know who say it takes much more insulin to come down from a large high than a smaller one.)
Obese Type 2s have a smaller beta cell volume than their obese non-diabetic counterparts
Keeping glucose levels lower in mice with pancreatic transplants improves the longer term prospects of the transplant
Cells in a dish do not like higher glucose levels
People with IGT get inflammation when they spike
Study not found but likely found that people with IGT are at a higher risk of neuropathy
Fasting Plasma Glucose (FPG) is a poor predictor of retinopathy
HbA1c is a better predictor of retinopathy than FPG and to get above the baseline risk, required an HbA1c of greater than 6% or a FPG of 6.5 mmol/l (around 120 mg/dl)
HbA1c above 6% has an increased prevalence of retinopathy with the threshold for significant risk at above 6.4%. For FPG the threshold was 6.6mmol/l (120mg/dl). OGTT proved to be a poor predictor.
The risk for retinopathy significantly increases when HbA1c is above 7% or when FPG is above 7 mmol/l (around 130 mg/dl). There is a smaller increase when the HbA1c is above 6.5%
Neither HbA1c nor FPG were seen as viable targets for heart failure therapy
Women with high glucose levels are at a greater risk of getting cancer
Cells in a lab will tolerate high levels of glucose exposure better than fluctuating levels of glucose
If there is an HbA1c threshold for chronic kidney disease, it is probably somewhere between 6-7%
A study exclusively focusing on non-diabetics using a non-standard OGTT. Therefore it is of limited relevance to Type 1s
You will see above that not one of these papers directly examined blood spikes over 110/120/140 mg/dl. The 140 mg/dl probably comes from the OGTT where IGT is defined as someone who has a blood glucose of 140 mg/dl after two hours. This says nothing about the number of glucose spikes a patient has had before the test or how high those spikes went. The 140 mg/dl limit in an OGTT tells us nothing about the level at which individual glucose spikes do damage to the body.
The 120 mg/dl may come from (15) where it was shown that an FPG above this led to an increased risk of retinopathy but a fasting glucose level says nothing about someone who is below this FPG level and occasionally spikes above 140 mg/dl.
As for the 110 mg/dl, I have no idea where this one comes from. Regardless, none of the 21 references provided evidence to support a “cogent case” that occasional spiking leads to long term damage.
So What Can We Conclude?
Summarizing my summary and including my original Swedish study, we get the following in regards to Type 1s and what blood levels make sense to stay healthy:
(Swedish study) An HbA1c below 6.5% increases the risk of severe hypoglcemia
(Swedish study) An HbA1c above 6.9% increased the risk of complications, including retinopathy
(Swedish study) There is an increased risk of preproliferative diabetic retinopathy above an HbA1c of 6.5%
(1), (2), (3), (4), (12) People who fail an OGTT have an increased risk of neuropathy
(13), (14), (15) FPG is a poor predictor of retinopathy but risk appears to increase above 120 mg/dl
(14), (15) HbA1c is a better predictor of retinopathy and risk increases above 6%, with significant risk above 6.4%
(16) There is a small increase in risk of retinopathy for an HbA1c above 6.4% with a significant risk above 7.0%
(16) An FPG above 130 mg/dl increases the risk of retinopathy
(20) If there is an HbA1c threshold for chronic kidney disease, it is probably somewhere between 6-7%. If there is no threshold, an HbA1c above 6% increases the risk
Clearly Fasting Plasma Glucose (FPG) and HbA1c have multiple studies examining at what point a diabetic has an increased risk of complications with retinopathy being a common complication studied.
Based on the above, it is clear that an HbA1c below 7.0% is desirable (Swedish study, (16)) and, likely, an HbA1c below 6.4% is better (Swedish study, (14), (15), (16), (20)). However, an Hba1c below 6.4% does put an insulin-dependent diabetic at an increased risk of a severe hypo (Swedish study) so, therefore, depending on how well you can manage the fluctuations may determine where your target HbA1c range will sit.
For Fasting Plasma Glucose, while not as strong a predictor as HbA1c, keeping it below 120 mg/dl would be prudent (13), (14), (15), (16).
Reviewing the multiple studies of a site which makes the claim that blood sugars above 140 mg/dl cause damage and worsen diabetes, not one directly studied meal spikes and their long term effects.
However, when these studies were combined with a recent Swedish study, we can conclude that keeping your HbA1c below 7.0%, and for those who have a low risk of hypo, below 6.4% will minimize the risk of complications, especially retinopathy.
For fasting plasma glucose (FPG), keeping this below 120 mg/dl (6.7 mmol/l) is also desirable to reduce the risk of complications but it should be acknowledged that FPG is not as reliable as a predictor of complications as HbA1c.
Finally, given there was no study examining the damage of meal spikes and assessing a ‘safe’ level, it is reasonable to ask whether religiously guarding your blood glucose levels is worth it; whether the mental fatigue of constant monitoring, and risk of burnout, is outweighed by the unproven benefits. Perhaps it is better to focus on longer term measures such as HbA1c, the standard deviation of glucose levels over time, and time in range. Perhaps it is better to see an occasional high spike as an unfortunate day on a much longer journey rather than as a defeat or failure.