Sunday, August 16, 2015

Diabetic Autonomic Neuropathy: where variability is a good thing.

Diabetic Autonomic Neuropathy: where variability is a good thing.

We are all familiar with the most spectacular complications of Type 1 Diabetes: cardio-vascular damage, nephropathy, neuropathy, diabetic retinopathy, etc... That's part of the basic information package every newly diagnosed patient receives, the Damocles sword that motivates us to control blood sugar. Most of them are, to a significant extent, linked to the glycation of proteins. They are typically called “advanced glycation end products” (AGEs) in the literature. In many cases, AGE have been directly linked to the damage observed. This is also the main reason why HbA1c, as an AGE, is such a good control indicator: it is a proxy for what happens elsewhere in your body. Good blood glucose control is the tool of choice to prevent or minimize a lot of the complications we are facing.

However, things aren't that simple. Diabetic neuropathy is often understood as peripheral neuropathy as in
“Uncle Jim lost sensations in his foot, he had blisters, they became infected and did not heal because his arteries were bad too. They had to cut his leg.”

That is not the whole story... enter the autonomic nervous system.

Our nervous system does not consist only of a cortex, a sensory sub-system and a motor sub-system. There's a thing called the autonomic nervous system that controls, mostly unconsciously and without our intervention, the basic functionality of our bodies: the rhythm of our heart, our respiration, vasodilation, vasoconstriction, the behaviour of our stomach, intestines and bladder, our reaction to exercise and stress, even sexual arousal...

While it is mostly invisible, the autonomic nervous system is what keeps us comfortably alive.

A detailed explanation of how the system works is, of course, outside the scope of a mere blog post. But one simple way to visualize the autonomic nervous system is to think of two separate controllers called the sympathetic and parasympathetic systems.
  • The sympathetic system would be most active in a “fight or flight” situation (increases heart rate, sends blood to muscle, redirects blood flow away from secondary functions, etc...).
  • The parasympathetic system would be most active in a “read and digest” situation (sends blood to intestines, increases peristalsis, decreases heart rate). 
Here is an illustration of the ramifications of the system (marked as free for non commercial use by Google Image search - do not hesitated contacting me if any of the republished illustrations are in violation of anything)

Most of the time, in healthy people, the systems are said to be “balanced”. The concept is a bit fuzzy: it basically means that the systems do what they have to do in an appropriate way. When the balance is lost through diabetic autonomic neuropathy, life can be hell. This often cited paper gives a good overview of the ton of severe issues it is directly responsible for. Warning: do not read it if you are the type of person that worries endlessly.


Roughly speaking, most of the wiring of the system goes through two big nerves: the vagus nerve and the splanchnic nerve. Some other smaller nerves such as spinal nerves serve other territories. The type of wire that goes into each nerve is a complex topic in itself, especially since the heart is a special case. No worries though, we won't need to go into details for our purposes.

If you have read the paper above, you have seen that, in diabetes, this system can be badly damaged. And to add insult to injury, while poor control has the obvious deleterious effects, it can be damaged very soon in the course of the disease and, apparently, somewhat independently of your blood glucose control.  How does that happen? Well, we don't really know. Just as we don't know why some nerves seem to be impacted more than others. Glycation as usual. Auto-immune reactions and inflammation do play a role, but beyond that, looking at the literature, it is again a depressing case of “probably affects”, “deserves further attention”, “seems to be implicated”...
It is the main actor behind gastroparesis, the delayed, inconsistent emptying of the stomach that can wreak havoc on the best control strategy. But it can also lead to orthostatic hypotension (low blood pressure when standing up from sitting), dizzyness, erectile disfunction, lack of exercise adaptation, etc... 

Unfortunately, the heart is also a target, so much that it deserves its own acronym: CAN for cardiac autonomic neuropathy ( CAN is also suspected to play a role in sudden death (certain for Type 2 Diabetics, may play a role in Type 1 Diabetics although ionic and pH disturbances my be enough by themselves)

The heart of the matter

Our heart runs a natural pacemaker, called the sinoatrial node. It triggers roughly 60-70 times per minute: that is, if you want, our natural spontaneous rhythm. Its activity is modulated by the sympathetic – parasympathetic balance. The parasympathetic impulses reaching the sinoatrial node through the vagus nerve tend to lower the rate at which the natural pacemaker fires. The sympathetic impulses, traveling through the spinal nerves, increase the firing rate and the strength of the ventricular contraction, for example when we exercise. In a healthy subject, the systems are ideally balanced.

However, if the vagus nerve is severely damaged, an imbalance is introduced: the sympathetic system will work almost as it should but the parasympathetic activity will be lower. At the extreme, an old diabetic will have resting heart rate higher than an healthy individual and will not adapt as easily to exercise or even suddenly standing up from a sitting position.

And that slowly brings us to the fancy world of tachograms and RR interval analysis

A healthy S/PS balance is always ready to react almost instantly to any change of conditions. The sinoatrial node is normally in a very unstable “trigger happy” state. Mere emotions can accelerate our firing rate within seconds. At the peripheral level, sudden vasodilation can make us faint. Run five steps, your heart responds at once.

That instability is highly desirable (an unusual concept for diabetics) as it reflects our ability to adapt to changes in life. You do positively want to have a constantly unstable heart. That variability can be quantified in a myriad of ways. It is loved by researchers as it gives them plenty of opportunities to publish papers on the correlation between dozens of indicators with dozens of outcomes under a dozen of circumstances such as post myocardial infarction, aging, exercise recovery, or even our propensity to socialize (where's free will anyway?) etc...

As far as the T1D patient is concerned, the story begins around 1975 when DJ Ewing  and others looked at RR variability (basically how unstable your cardiac rhythm is) and published this paper  which can be summarized by this figure
The hearts of diabetics did not seem, on average, to behave like the hearts of healthy controls.

Very quickly, lots of people jumped on the concept, confirmed the findings (here for example, here) and the concept was used as a mortality predictor (here for example) and as a tool to detect early asymptomatic autonomic neuropathy (see again the Vinik paper for explanation and links).

As a T1D parent, I am always a bit paranoid. Once I learned that diabetic autonomic neuropathy could potentially exist at the time of diagnosis, I absolutely, totally and utterly needed to know if my son was affected. On the basis of that paper that, armed with an ECG device, I embarked on what I expected to be a simple check and ultimately was dragged into a tricky journey in the very muddy waters of RR Interval Analysis, tachograms, power bands and clinical protocols (or the lack of them)

That will be for the next post.

Thursday, August 6, 2015

Meter vs Meter or a quick shot at some Internet and marketing diabetic memes.

What about the BG meter issues?

What do we actually measure?

Well, in principle, a BG meter test measures capillary blood glucose. It is constantly changing, some times very quickly. It differs from interstitial glucose, venous, arterial blood glucose, etc... On top of that, the differences aren't static. Think about it in terms of shifted waves going up and down. Complex? Yes. But even that is a simplification: think about them in terms of shifted going up and down where the shift is not constant. Going that deep isn't very useful. What is useful is a reasonably representative snapshot of some value you want to keep in some range. There is no need to hunt for the perfectly accurate glucose value, it doesn't exist. 

Back to BG meters

What do I want from a BG meter? Within limits, I don't care that much about accuracy: if the reader tells me I am at 90 mg/dL when it should have measured 100 mg/dL. 110 mg/dL is also fine. I am measuring a fleeting local reality that does not exist as an absolute truth. What I do care a lot about is precision, consistency. If my fleeting reality was at 100 mg/dL and just dropped to 80 mg/dL, my precise but inaccurate BG meter would tell me that I fell from 90 mg/dL to 72 mg/dL while an accurate but imprecise reader could have given me a stable value.

Of course, ideally, you would want a BG meter that is both precise and accurate. But a device that is biased consistently 10 mg/dL lower at +/- 5% is obviously less dangerous than a perfectly calibrated device that works at +/- 15%.

Internet meme 1: "Your reader is only accurate +/- 20%"

Where does it come from? A misunderstanding in the coverage by most diabetic sites of the ISO BG meters criteria and tests that basically state that 95% of the time, the results should fall withing 20% (or 15%) of the "correct" value. 

How is it typically interpreted? As "The result you got is +/- 20% anyway..." 

I am sorry to say that it is total bull****. Anyone with a basic high school statistical education has been given a free hint with the 95%.

Lets look at a real example. Here is the data of the 38 double BG meter tests we did, within a 120 seconds interval, since January 1st 2015. I am actually cheating a bit here, we did 40 double BG meter tests but we'll get to that later. These tests are the ones we did for the initial calibration of the Dexcom sensor and random double checks we did when we just wanted to be sure.

[70, 90, 95, 65, 84, 242, 81, 69, 119, 88, 110, 109, 85, 66, 182, 162, 245, 53, 55, 111, 140, 119, 170, 56, 77, 80, 234, 78, 55, 129, 79, 93, 77, 88, 135, 77, 77, 124]

[64, 85, 104, 60, 91, 268, 82, 78, 108, 74, 102, 108, 86, 64, 189, 147, 240, 47, 58, 100, 134, 109, 160, 54, 81, 89, 206, 75, 55, 128, 93, 89, 75, 91, 146, 76, 69, 130]

Here are the differences

[6, 5, -9, 5, -7, -26, -1, -9, 11, 14, 8, 1, -1, 2, -7, 15, 5, 6, -3, 11, 6, 10, 10, 2, -4, -9, 28, 3, 0, 1, -14, 4, 2, -3, -11, 1, 8, -6]

Let's plot that data in terms or error percentage. Does that ring a Bell?

Even if you know nothing about statistics and don't recognize the curve, you can't fail to notice that most of the results will be found in the +/- 10% range. Strictly speaking, we can't say on the basis of that sample alone that the distribution is purely normal but it is certainly much closer to normal than a random +/-20% error would be. My hunch is that it is essentially normal, plus a time drift (BG can change in 2 minutes), plus an "accidental" component.

I said above that I removed two data points. One of the tests we did was, in fact, a triple test. Why? Because it was very visible that there wasn't enough blood in the well. That test gave us 101 mg/dL while the two controls with enough blood gave us 124 and 130 mg/dL.

The other removed test is more interesting. What would have happened if we had included it?

The values returned by the two BG meter tests were 283 mg/dL and 24 mg/dL. Something was wrong. And that something was a failing battery in the BG meter.

That illustrates the fact that while BG meters will generally deliver results around the fleeting "correct" value, they aren't immune to extra-ordinary errors. Dextrose powder on the fingers, water, lack of blood are typical factors that will lead to inconsistent results. The list is long but, in practice, most of them are available (the topic of another blog post maybe).

At this point, I hope I have put the "anything +/-20%" Internet meme to rest. It should be rephrased into something like "very often quite close, sometimes 20% off, potentially anywhere if not used properly"

The emerging marketing meme

Now, let's have a look at the currently emerging meme: "CGMs are now more accurate than BG Meters".

Before I start, I'd like to stress that I am totally convinced that CGMs are the best tool to manage your diabetes. I can't stress that enough. But the reason why they are the best tool is not that they are more accurate. The reason is that they allow patients to understand how their diabetes work, how their body reacts to meals and exercise. 

But that is not necessarily how they are marketed. Dexcom said in one of their conference calls (I summarize) that CGMs were now more accurate than BGMs, opposing their best MARD (around 10-11%) which most people don't get in real life to the above +/-20% Internet BG meter meme.

The problem is that the current G4 needs BG meter calibrations. You can't logically claim that a measuring instrument B calibrated with a measuring instrument A will be more accurate than the instrument A. 

The eventual unavoidable systemic error in instrument A will be added to the error of instrument B in a complex way (error chaining analysis). Even if you are using "optimal" calibrations you will still introduce a bit of additional error, as Abbott has apparently shown in its Libre papers.

[Note: can be skipped if you don't want to nit pick... You could actually calibrate a device B with an inaccurate device A and get the device B to perform much better than device A if you do a large number of tests with device B. If you do 2, 4, 8, 16, 32, 64... inaccurate tests you will reduce the error by a factor of 1.4 at each step. Unfortunately, unless you want to do a lot of simultaneous blood tests, you are unlikely to approach perfection.]

And lastly

How did the BG meter worked vs itself in a more conventional medical view? In other words, how consistent was it?

In other words, excluding any bias, our BG meter works within the latest ISO spec in terms of result consistency and the ISO spec does not mean that its results are randomly distributed in the +/- 20% (or 15%) range.

Wednesday, August 5, 2015

Predictive is the new buzzword: Libre, Dexcom and Roche...

In November 2014, when we received our first Libre sensor, I was immediately impressed by its uncanny ability to closely match its spot checks to our BG meter values. The "keep the delay in mind" had been our mantra with the (non AP) Dexcom G4. Our first interesting test - a post meal increase - came up 14 hours after our insertion and blew me away.

 It almost immediately seemed a bit "fishy". Surely, the Libre wasn't immune to the average BG-ISIG delay. Could it be that it was actually wrong but lucky? But this kind of freak occurrence repeated itself throughout our Libre phase. The Libre was systematically ahead of the Dexcom G4. After the initial lowish startup few hours (a behavior that was seen in all our sensors except the ones we pre-inserted) it caught up and started to be ahead.

However, spot checks remained peculiar. Correct most of the time, but with a distinct overshoot in situations of fast rising BG. I couldn't shake the feeling that something strange was going on. This "paranoia" was also fueled by the observation that the Libre historical "average" value wasn't written at the end of the period, but after a certain delay. The Libre became, at least in my mind, the first "revisionist" CGM. Some of the spot checks did not materialize in the period averages but did fit quite nicely with what a predictor would have calculated.

At that point, I became convinced the Libre spot checks were, in times of changing BG, predictive.
As an amateur observer of the tools we use to manage our diabetes, I was also a bit shocked by the use of prediction. I guess that my expectations were that a CGM would try to match its current reality as closely as it could and that was it. None of the Abbott's material mentioned "predictive" as far as I could tell. Interestingly enough, the Libre RAW data that I had been able to mostly understand in December wasn't always showing these spikes either. Approximate averages of RAW closely matched historical values, more than they did match the "over-shooters" spot checks that did not ultimately materialize in  BG. Still I thought I was wrong, I had missed something and kept looking.

I then stumbled upon a paper that described how Abbott compensated for slopes in its Navigator II calibration algorithm. (FreeStyle Navigator Continuous Glucose Monitoring System with TRUstart Algorithm, a 1-Hour Warm-Up Time)

If it was kosher to compensate for lag in a calibration algorithm, the next natural step was to display  projected values to patients... As it turns out, using a predictive model allowed my raw data interpretation to closely match what the Libre displayed and understand odd, potentially dangerous behaviors such as the one described in the "meal and bath" incident. As an informed patient, that incident annoyed me profoundly: it is one thing to rely on actual measured data and another to rely on projected data that is equivalent 80% of the time, better 19% of the time and outrageously wrong 1% of the time. Your mileage may vary and this probably doesn't matter much in the grand scheme of things for the general T1D population, but still.

Back to Dexcom

This may have been one of the reasons we went back to the non AP G4 Dexcom, along with the obvious Abbott sensors availability issues. But then, I missed the quick reaction time of the Libre. To some extent, using xdrip solved the issue partially as it allowed me to get rid of the non AP G4 algorithm induced delay. But it also incited me to hunt for possible improvements on the G4 reaction time by using, you guessed it, predictive algorithms...

Now, before one gets too excited about the results, I'd like to insist that my "work" has been of the dirty, inconvenient, unpractical kitchen sink type of work. The first constraint is, of course, to have access to the dexcom secondary raw data in real time through xdrip. The dirty part involves artificially tampering with the 5 mins data frequency of the Dexcom. I needed value every minute and decided to interpolate the min by min data between Dexcom readings. That is not very clean but, based on the resampling done when comparing the Libre and Dexcom 14 days run, it doesn't seem to have any impact on the big picture. Then, based on that minute by minute data, I started issuing "predictions" of what 9 minutes later would look like and how it would match BG meter readings.

Here are the results: in both cases, my kitchen sink approach was able to drive the non AP G4 MARD from above 10% to below 10%.

Two points worth noting:
  • the resampling adds or removes a couple of points. This happens because my BG meter clock doesn't have second resolution and drifts a bit. Since I use the closest previous CGM data points provided it is withing two minutes on the BG test and since the granularity of the prediction is 1 minute, a few points drift in and out of the window. This has no impact on the results in one case and actually worsens it a bit in the other case.
  • the predictive algorithm usually improves accuracy but worsens it in some cases (not unlike the Libre in fact)
Anyway, since this is a kitchen sink experiment, who cares...



But things start to get interesting with the Roche sensor. The Roche sensor is a bit of a "Loch Ness" sensor. Roche published material talking about Artificial Pancreas Development in 2003, supported by a micro dialysis CGM sensor. Twelve years later, they are still talking about it, but outside of a few clinical tests, I don't think many people have seen the beast. Micro dialysis has a few advantages over glucose oxydase based sensors, but also a few inconveniences. It relies on a flow of ringer through a double lumen catheter and the ringer, as I understand it, doesn't recycle itself and must be discarded. Even at a few micro-liters per minute, that puts some limits on what it achievable in the form factor diabetics are now expecting from their CGMs. Plus there is the issue of generating the flow.

The Roche sensor claimed extremely good results in that paper in 2013 (so much that I was expecting it instead of the Libre in 2014), was used in AP tests in 2014 And come 2015, new sightings of the monster have been reported. In this paper, for example, where it is reported to track rapid changes much better than the Dexcom G4: Rate-of-Change Dependence of the Performance of Two CGM Systems During Induced Glucose Swings.(Pleus S1, Schoemaker M2, Morgenstern K2, Schmelzeisen-Redeker G2, Haug C3, Link M3, Zschornack E3, Freckmann G3.)  

Hmmmmm, tracking rapid changes much better than the Dexcom? Where have we seen this before?

And where does the magic come from? Can you guess? A second paper by some of the same authors provides the answer: Time Delay of CGM sensors Günther Schmelzeisen-Redeker, PhD1,Michael Schoemaker, PhD1 Harald Kirchsteiger, PhD2 Guido Freckmann, MD3 Lutz Heinemann, PhD4 Luigi del Re, PhD2

And the answer is: predictive algorithms.

In an environment where the vast majority of endocrinologists are still somewhat uncomfortable with basic CGMs, devices that often do not actually display a measured value but show a predicted one could be a tough sell. Fortunately, the vast majority of practicing endocrinologists will neither have the time nor the desire to explore the darkest recesses of the technology behind their tools and they won't know.

PS: and yes, I am aware that yet another player based on another technology (senseonics) has also published significant results. But at this point, I have mixed feelings on the potential scarring.