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).”
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 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
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%.
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
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.
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?
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.
Our family recently did a big trip to the USA from Australia.
The trip had us go through customs in:
Sydney, Australia -> Hawaii
Hawaii -> New York via Los Angeles
New York -> Los Angeles
Los Angeles -> Sydney, Australia
I thought this would give me a good opportunity to try out carrying my insulin using a vacuum flask. I could test the flask in a real world scenario and see how it fared through customs. This was the rig I used.
The digital display is for a temperature sensor inside, held onto the flask with a rubber band. Yes, it looks like a pipe bomb (or a sex toy according to some of the folk on Facebook but that probably says more about them than the rig). Please note it is recommended that insulin is taken in your carry-on luggage as the temperature in the cargo hold is not as predictable and frozen insulin is useless.
Some of the predictions from the JDRF 25+ Facebook group (where I posted my intentions) included:
“I’ll be surprised if you don’t get shot in the US with that… Any cylindrical tube with a screen and wires on it will cause a lockdown.”
“Expect long delays”
“Kinda glad I’m not flying anywhere today as the airport will probably go into lock down… if it somehow gets through Australia I guess we will see on the news tomorrow of ‘Australian shot by American airport police and airport in lockdown dew to fake bomb found’ “
Given I am writing this blog I clearly did not get shot. So how did I go?
Sydney -> Hawaii
At every location where an x-ray of bags was happening, I took the rig out of my bag, put it in the tray and alerted the security officer that it was insulin.
In Sydney, this information brought a smile and the response “You know what it looks like, right?” Knowing full well one does not say the b-word (bomb) at an airport, I acknowledged I did and he waved me through. He also told me that insulin is allowed to be carried through Australian customs with ice or gel packs in the special case of diabetics.
As I walked through the metal detector, the officer called his colleague over to watch the x-ray screen, using my device as an exercise to show the difference between insulin pens and incendiary devices.
All went well.
For the record, the officer is correct in regards to gels and ice packs, although finding the details on the Australian Home Affairs site is difficult. Eventually I found this page which quotes:
“If you plan to bring medication onboard, remember to:
obtain supporting documentation, such as a medical identification card or a letter from a doctor. The letter should itemise any prescription and non-prescription powder, liquid, aerosol or gel medication, prescribed medical devices or equipment, for example, ice or gel packs used to regulate temperatures, or the need for hypodermic needles.
have medication and accompanying documents ready for inspection before you arrive at the airport security screening point.
For prescription medication, make sure the name on the prescription label matches the name on your boarding pass or the name of the person travelling in your care.”
The medical identification card for Australian diabetics is the NDSS card. You will know if you have one. Interestingly, while I was carrying a letter from my endocrinologist, it did not explicitly say the insulin needed a gel or ice pack to keep it viable. Also, while I do carry prescription pills, the doctor’s itemised list of medications I carried was old and did not correctly list them.
In principle, a fussy customs officer could have confiscated any ice packs I was carrying and confiscated my pills. This did not happen, thankfully.
While not explicitly stated on this site, the NDSS site states diabetic medication is exempt from the 100mL rule for liquids (although it does need to be presented at the security point). My endocrinologist letter also mentioned my need for juice boxes so, based on the above, these would also be exempt.
Landing in Hawaii there were no scans or checkpoints so things were uneventful at that end.
Hawaii -> New York via Los Angeles
As with Sydney, I pulled the device out of my bag, put it in the tray and announced what it was. While my pipe bomb was not used as a learning opportunity for another officer in Hawaii, they were fine with me carrying it through. They did insist I open it up for a visual inspection and asked if there was anything sharp in there. There were the needles but, as I explained, they are all sealed up and safe.
After a quick visual inspection I was good to go. Again I asked if insulin can be carried through US customer in ice or with a gel pack and they confirmed it can be.
This is confirmed on the TSA website. Also, the Medtronic web site says if you encounter problems to ask to speak to the TSA Ground Security Commissioner.
New York (JFK) -> Los Angeles
Mostly incident free. I put it in the tray, said what it was and they did not bat an eyelid. They did ask me what the device on my arm was which, I explained, was a CGM for monitoring my blood sugar. They accepted this and waved me on.
Los Angeles -> Sydney
Almost incident free. It turns out the coconut flavored peanut butter I bought in Hawaii is considered a liquid/gel and being over 100mL (3.4 ounces) it had to be confiscated. I tried playing the diabetes card but to no avail. Peanut butter was not considered essential to my medical condition (it certainly is not mentioned on the doctor’s list of medications.)
How Did The Container Fare?
My initial tests at home with ice inside were promising. The temperature stayed cool for over a day. On the trip, as the container had actual insulin pens in it, I was reluctant to put ice in there as well (frozen insulin = bad insulin). So there was nothing to keep the insulin cool in the wild. I simply removed it from the fridge at home and took it on the flight with a view of putting it in the fridge on arrival. This plan worked fine, except the initial leg (Sydney to Hawaii, 9 hour flight). By the time we got to the house where we were staying, the internal temperature was matching the outside (28C/82F). This is at the upper limits for insulin so on my next trip I will see how gel or evaporative cooling fares. However, for shorter trips, the vacuum flask would work fine and is easy to carry.
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.
If you are a Type 1 diabetic then you will know the truth is there is a myriad of things which can affect your blood sugars and they are inherently unpredictable. It reminds me a little of the stock market. It is impossible to predict what the final price of a stock will be on a given day but you can predict the trend over time. Anyone that tells you differently is, most likely, trying to sell you something.
So too with with blood glucose. It is very hard to accurately predict your body’s reaction to the various forces acting on your blood on a given day but, over time, you can get an idea of the trends and general principles. Anyone that tells you otherwise is probably selling a book or supplements.
The upshot of this is that you should never beat yourself up for having a bad day with your blood sugar. Focus on the game and not a given play. Let measures such as your HbA1c, percentage in range, and the standard deviation be your guide more than a moment in time on your glucometer.
Knowing how different forces act on your blood glucose can help you manage these long term trends so here are some of the big influences on your blood sugar. As usual, there is a tl;dr summary at the end for the time deprived.
Clearly one of the biggest forces on blood glucose are carbohydrates. We can divide carbohydrates into three categories for this discussion:
Fast Acting: Sugars/Simple Carbohydrates/High Glycemic Index (GI) Foods
Slow Acting: Starches/Complex Carbohydrates/Low GI Foods
No Acting: Inedible Carbohydrates/Fiber
Fast acting carbohydrates will spike the blood and make it very difficult to manage. If you take insulin you will need to try and match the insulin activity with the blood sugar spike. Get this wrong and your blood sugar starts roller-coasting. For the diabetics who can still produce their own insulin, fast acting sugars in sufficient quantities can overwhelm your pancreas and spike your blood.
Slower acting carbohydrates still need to be covered by insulin but the slower rise can make it easier to manage. The slower rise also means those with an impaired pancreas may be able to produce enough insulin to stop them spiking too high.
Fiber, by definition, is not broken down by the body so it is physically impossible for it to directly affect blood sugars. This being said, there are insulin dependent diabetics who factor fiber in their calculations. My guess is because, in certain countries like the USA, food labeling puts fiber in with the rest of the carbohydrates so it simply makes things easier to calculate a ratio including the fiber, even if it makes no metabolic sense.
There is no direct metabolic path to convert digested fat into glucose so eating fat will not raise your blood sugars. However, like fiber, it will slow down the absorption of carbohydrates. Also, digested fats readily enter the bloodstream, temporarily increasing insulin resistance. This leads to some people concluding it raises blood sugar when the reality is their insulin is simply not as effective.
As an example, let us consider a slice of toast with 15g of carbohydrates. While this may normally require ‘x’ units of insulin to be covered, if it is eaten with avocado on top, which is 15% fat, this may raise insulin resistance and the normal amount of insulin will be insufficient, leading to a spike. It is not the fat turning to glucose which causes this spike, it is the temporary increase in insulin resistance. Also the insulin resistance may influence the effectiveness of a Type 1’s basal insulin leading to increased glucose output by the liver.
Usually proteins will not significantly affect your glucose as the body does not convert a lot of protein to glucose but, for someone eating a low carbohydrate diet, the body ramps up a metabolic process called gluconeogenesis which is the one which converts protein to glucose. There is no hard rule for bolusing for proteins under these circumstances but some find success by finding a ‘protein ratio’ similar to their insulin to carbohydrate ratio.
This is a bottle of almost pure alcohol (95% alcohol by volume, I use it to make sugar-free liqueurs). Like fats, alcohol does not convert directly to glucose so, in principle, it will not affect blood sugar. However, also like fats, this is not the full story.
Alcohol is seen as a poison in the body so the liver will drop everything to remove it from the blood. This includes releasing glucose into the blood which, usually, the liver does constantly and is the reason Type 1 diabetics use a long acting insulin to keep their liver from releasing too much glucose (basal insulin).
So, in theory, alcohol will lead to a drop in blood glucose but no one drinks pure alcohol. Liqueurs usually contain sugar syrup (sugar dissolved in water) and the fermentation process demands the use of sugars to feed the yeast and residual sugars often end up in the final product.
So alcoholic drinks are a mixed bag. The alcohol has the potential to lower blood sugar but the things it is mixed with may raise blood sugars. This makes alcoholic drinks quite dangerous for Type 1s in large quantities because the effects are inherently unpredictable.
In the short term, aerobic exercise will lower blood glucose as the body makes use of it to run muscles. Moreover, it is believed that the glucose enters the muscles through pathways opened up by exercise which do not require insulin. Short term anaerobic exercise (more strenuous exercise) can raise blood sugar as the liver releases glucose into the blood to help feed the muscles.
Also, the effects of exercise on the blood can continue well after exercise has stopped so monitoring of blood glucose is very important.
In the long term, exercise can reduce fat stores in the body, lowering insulin resistance, as well as increasing muscle mass to store glucose.
The human body is a complex interplay of hormones so when one increases or decreases, this has an effect on others. Insulin is no exception. For diabetics going through puberty this is a minefield. For women, their monthly cycle can also cause insulin resistance to fluctuate, throwing out insulin ratios and interfering with blood glucose management.
Stress and Illness
Given both stress and illness affect hormones in the body, it is unsurprising that they affect blood sugar levels. Try to avoid unnecessary stress and carefully check blood glucose levels during illness. Strategies which work to reduce stress hormones and lower blood glucose for some include meditation and massage.
The act of going to sleep can affect your blood sugar or, more accurately, the act of the waking up. Dawn phenomenon is an increase in blood sugar (probably) due to the shifts in hormones as someone moves from sleep to being awake. There is not a huge amount that can be done about the dawn phenomenon but, if it is causing blood glucose to be consistently high in the mornings at a level to potentially cause long damage to the body, it may be worth discussing, with your health care team, changing your basal insulin routine.
Medications can affect blood sugars with a common complaint coming from the injection or ingestion of steroids for medical treatment. Steroids tend to spike the blood, especially if injected. If you are taking a new medication, it makes sense to ask your health care team how it may affect your blood sugar and insulin resistance.
Strategies For Management
As you can see above, there are a range of factors which can affect blood glucose levels. For this reason, an approach which primarily relies on looking at food, such as Dr Bernstein and Forks Over Knives, may well work for some but has no guarantee of working for everyone.
In my opinion, a better approach is to adjust insulin, rather than trying to adjust everything else. Looping (the linking of a continuous glucose monitor and an insulin pump for automatic blood glucose management) and books like Sugar Surfing or Think Like a Pancreas adopt this approach.
This being said, diet moderation still has a place. Modern insulins have their limitations so it make sense to be careful not to test those limitations with a diet filled with fast-acting carbohydrates.
A healthy human body does a remarkable job of keeping blood sugars in check. For those of us with impaired or non-existent insulin production replicating this job can be very hard. With so many factors affecting blood sugar levels, it is impossible to have perfect management every day. Therefore, it is better to manage glucose levels for the long term, rather than fixating on how your levels are on a given day.
Some of the factors affecting blood glucose are:
Fast and slow acting carbohydrates: Increase blood glucose
Fiber: No direct effect on glucose levels but can slow the absorption of digestible carbohydrates, stretching out the blood glucose response curve
Fats: No direct effect on glucose levels but they can temporarily increase insulin resistance leading to increased liver glucose production, and a spike in blood glucose in response to food as the on board insulin proves to be not as effective as it otherwise would be. In contrast, like fiber, fats can slow the digestion of glucose, widening the response curve.
Proteins: No direct effect unless the person is not eating enough carbohydrate for their body’s needs. In this case, the body uses gluconeogenesis to directly convert proteins to glucose, raising blood glucose levels
Alcohol: While pure alcohol has the potential to lower glucose, not many people drink pure alcohol. Many people drink either a fermented drink like beer which has sugar as an integral part of the process or they drink liqueurs which are a combination of alcohol, sugar syrup and flavoring. Therefore, depending on the alcoholic drink at hand, it can raise or lower blood glucose levels
Exercise: Gentle (aerobic) exercise usually lowers blood glucose while more strenuous (anaerobic) exercise can raise blood glucose levels. In the long term exercise can be beneficial in reducing insulin resistance and increasing muscle mass, used for glucose storage.
Hormones: Different hormones of the body can also affect insulin resistance and, therefore, blood glucose levels. This is especially problematic for diabetics going through puberty and for women who are menstruating
Stress, Illness, and the Dawn Phenomenon: As all of these affect the body’s hormones, it is no surprise they also affect blood glucose levels
Medications: These can affect the body and blood glucose levels. If you are unsure if a specific medication will affect you, talk to your health care team
There are a range of factors which can influence blood glucose levels and, often, in unpredictable ways. While some advocate for strictly controlling major factors, such as food, others advocate adjusting insulin to accommodate all of these influencing factors. Every person is different so it will be a case of taking the elements from both approaches which work for you.