Before I begin addressing one of the issues that intrinsically impacts all the third party Freestyle Libre applications I am aware of, let me restate a few things
- Back in early 2015, I had a decent (for my purpose) solution that I felt would not extend well to others. That is mostly why I did not release it.
- I supported and still support the BlueReader project. I am not against open source solutions. When the BlueReaders I ordered arrive, I will certainly enjoy experimenting with them. I will, however, not rely on them for my son, even if the Libre becomes our main monitoring solution.
- I stopped my Libre investigations in early 2015, both because obtaining sensors from France was a drag and because what I learned in the process of investigating the Libre had helped me improve the results I got from the G4. I am just resuming them in preparation of the BlueReader.
- piggy backing on the Libre sensor doesn’t sound like an attractive solution as far as I am concerned as it is only likely to increase micro traumas issues.
- There is a fundamental difference between what the community calls “raw” from the Dexcom and the “raw” one gets from the Libre. In a nutshell, the Dexcom “raw” is not raw. The Libre “raw” is raw.
Temperature againOne of the things that differentiates the Dexcom “raw” from the Libre raw glucose data is that the Libre data is not compensated for temperature: that is the job of the reader or the phone application, working on the thermistor data.
Does it matter? Let’s start with a small explanation intended for software engineers who don’t have a biochemistry or medical background. Sensors rely on an enzymatic reaction. The enzyme used is often glucose oxydase (but it is not the only enzyme that would work, some BGMeters use hexokinase for example). The Senseonics sensors will use a different method based on fluorescence.
The activity of enzymes vary greatly depending on the local concentrations (Michaelis Menten kinetics), the pH and on the temperature. The local concentration issues are usually solved by the sensor membranes: they control the concentration of glucose and oxygen to match, as best as they can, the linear response zone of the Michelis Menten equation with their sensors. This video on amperometric glucose sensing is a must watch if you are interested
The pH is usually stable, except in DKA, of course, but at that point you have other things to worry about than your sensor’s accuracy (the acidosis can be so bad that standard laboratory tests may become unreliable). That is one aspect third parties don’t have to worry about much.
Temperature: does it matter in theory?But what about the temperature? Intuitively, one may think “well, I guess it doesn’t matter much…”. Or, if you care, try to address it in a generic way. One of my first ideas, back in 2014-2015, was to get a some “glucose oxydase activity curve as a function of the temperature” and compensate for that.
Unfortunately, both the “I don’t care” and the “I will fix” ideas are wrong, as it can be seen in the image below (from this paper)
The relative activity of glucose oxydase varies immensely according to the pH but, as I said, we don’t care much as the pH variations shown here are well outside the physiological range. The temperature: we must care a lot about because the variation of activity can be is drastic within a few degrees and, finally, the way it is attached (difference between blue and red line). While it may be possible to find the exact temperature dependent response curve of Abbott’s wired enzyme, I suspect it is not publicly available and it would have to be experimentally derived. That realization killed my somewhat naïve hope to auto-magically correct the reported activity as a function of temperature. As far as some open source projects are concerned, not worrying about temperature compensation is a classical example of not knowing enough to even know there could be a problem. It is only when you know a bit more that you realize, to a better extent, that you don’t know.
Does it matter in practice?Armed with a bit of side knowledge in the dirty details (more about this later) and a basic understanding of what Abbott is doing, I ran more specific experiments to investigate the response to temperature changes, the correction and the delay applied to the correction. Here is such an example.
I spent some time in a warm bath, enough to significantly increase my subcutaneous temperature and the enzyme activity. My real BG before the bath (as measured twice with the Abbott BG Meter) was 106 mg/dL, my BG after the bath was 109 mg/dL (shown as the star below). In normal, uncorrected, conditions, my calculated ISIG with the parameters I derived for my son would have been around 130 (not ideal, but there are temperature differences between my son’s skin and mine, we’ll get to that later). In relative terms, the response to my stable ISIG almost doubled. One word of caution, listing all the situations, bath temperature, submersion or non submersion of the sensor, etc… would be way too long for this blog and way to tedious for me to detail: let’s just say you don’t want a video of my person wiggling in a bath.
t2, shown here, is the temperature reported by the board thermistor. The Libre “official” check (round dot) sat between the direct conversion value and the SMBG value. Clearly, there is some compensation going on.
Let’s talk a minute about the thermistors, which I have arbitrarily named t1 and t2, the “close to skin” and “TI board thermistor” respectively and summarize what I know about them.
t1 – I have measured several thermistors and derived values from my observations, from which I have build a response curve. Limitations are: 1. I am not sure Abbott always uses the same thermistor as I have measured different values. This could be a variation in the thermistors delivered to Abbott, a simple factory/software configuration option, a defect. 2. I don’t have a direct way to measure the exact temperature the thermistor is reporting when applied as I can’t sneak another thermistor under the patch. 3. I can obtain reasonable values by tweaking and twisting the data, but I am not sure Abbott tweaks it the way I do.
t2 – I am quite sure of that one. With a bit of bit tweaking, it does match neatly with board/package temperatures that I can more easily measure. Limitations here are: 1. I do not know if Abbott interprets it the way I do. They could be seeing 32.5C when I see 33C or the opposite. This matters for additional adjustments for further processing, especially any eventual derivative based prediction methods. 2. temperature variations may be smoothed (per my observations and the Abbott patent).
Their respective responses are roughly (disregard the y-axis units, there is some post processing involved, the internal thermistor response isn’t linear, even if the line may make it appear so)
In the graph below, you can see what happens when I cool the sensor with ice for a few minutes after the bath. The board temperature falls precipitously, while the speed of the actual reaction continues to decrease (I did not cool my skin/core in this case, but the warming of the bath is beginning to wear out). My BG (the star) is still stable, the official Libre value is now 147 but the Libre is clearly (flags, not shown here) losing his marbles and will go error 373 for a while after that torture.
In order to make the difference in behavior visible, here are a couple of snapshots in stable conditions
I did run a few tests in stable conditions in order to address any doubts I had on my thermistor interpretation. Here is another external sensor warming
and another, less aggressive, external sensor cooling
As I said above, there is a delta between my direct interpretation and the direct interpretation of the parameters I derived used quite successfully for my son two years ago – here is an example from back then that matched exactly, in stable temperature conditions.
and another example
and a new view of the meal and bath incident (changing temperature conditions on that same sensor I correlated perfectly with)
and the original report (early to 2015) of that meal and bath incident and my interpretation of it.
At this stage, key points to remember
- temperature variations can’t be ignored and may have a major impact on the signal.
- my son’s parameters are different from mine and can be explained in great part by different skin temperatures (but I haven’t validated that by actually measuring it under the sensor).
- temperature compensation effects may/often are delayed until some threshold has passed and the Libre considers it as real.
In the next posts (when time allows), we’ll talk a bit more on how Abbott tackles the problem of finding the temperature of the sensing site and how it differs from Dexcom’s temperature compensation. The exploitation of the double thermistor design is really fun and interesting (well, at least to me) even if I feel that it does not work too well in some cases. It seems there is a simpler (but much more expensive for Abbott?) way to address that issue. We’ll get there… (some time…)
That’s all for today.