#165 - AMA #24： Deep dive into blood glucose： why it matters, important metrics to track, and superior insights from a CGM
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Summary
In this episode, Dr. Peter Atiyah is joined by Dr. Bob Kaplan to tackle a series of questions that focus on glucose homeostasis. Why should one wear a glucose monitor if they don t have Type 2 diabetes or Type 1 diabetes? And what can we do to improve their glucose numbers?
Transcript
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Hey everyone, welcome to a sneak peek, ask me anything or AMA episode of the drive podcast.
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I'm your host, Peter Atiyah. At the end of this short episode, I'll explain how you can
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access the AMA episodes in full, along with a ton of other membership benefits we've created,
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or you can learn more now by going to peteratiyahmd.com forward slash subscribe.
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So without further delay, here's today's sneak peek of the ask me anything episode.
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Hey everyone, welcome to AMA number 24. In this episode, I am joined as usual by Bob Kaplan,
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and we devote the entire episode to a series of questions that focus around glucose homeostasis.
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We centered the discussion basically around the idea of why one would wear a CGM, especially
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someone who does not have type 2 diabetes or type 1 diabetes, and we get into the really deep nitty
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gritty around what is it about glucose that matters so much with respect to health? Why is it that I
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make such a stink about having lower average blood glucose, fewer peaks of glucose, less glucose
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variability, and all of the associated things that go with it? So I hope you'll check out AMA number
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24. And without further delay, here it is. Hello, Peter. Hey, Bob. How's it going? It's
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going well, man. Ready for an AMA? Ready as always. All right. So in this case, we got great questions
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about glucose, and we aggregated a bunch. I think it'd be good to do a deep dive. I'm going to go through
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a couple of the questions here and see what you think. So the first question is more of a statement
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than a question. I've heard Peter talk about how fasting glucose and even HbA1c measurements can
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often be misleading and how he favors OGTT, which is short for Original Gangster Time Trial. That's
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right. Yeah. Okay. Perfect. I think it might be oral glucose tolerance test with insulin measurements
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and also wearing a CGM to get a better sense of glucose homeostasis. My understanding is that OGTTs
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and CGMs are typically reserved for people with diabetes. So he's got the following questions.
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Why does Peter find these tests useful in quote-unquote healthy people? What is Peter looking
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for when assessing someone's glucose levels? What does he like and hate to see? How does Peter define
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normal versus abnormal control of glucose? If I'm not diabetic, do I have anything to worry about
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here? And there's another question that was, are you able to do a breakdown of what you look for
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on different people's CGM data and what you would advise to improve their numbers, similar to the AMA
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you did on lab tests? All right. So I'm going to pause you right there, Bob, and I want you to answer
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this question for me, honestly. Did you pay this person to ask these questions?
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Asking for a friend. I mean, seriously, these are the perfect questions, the most salient questions,
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the most important questions. And this might become by extension then one of the most important AMAs we do
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in terms of the aggregate impact it could have on health and longevity. Because these questions
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really get at the root of where I think, I hate to use this term, but for lack of a better word,
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where the mainstream medical system is just so out of sync with what I believe the future of medicine
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is going to be. So let's take a step back on all of this for a second. Type 2 diabetes has a
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definition, and it is defined as having a hemoglobin A1C concentration greater than 6.5%.
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And that corresponds to an average blood glucose. God, I should know this, but the fact that I pay
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so little attention to it tells you why I don't even know it. I believe it corresponds to an average
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blood glucose of approximately 130 milligrams per deciliter. And of course, the way it works is it
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measures the concentration of glycosylated hemoglobin. So it's taking out red blood cells
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and it's looking at how much glucose is stuck to them. And obviously the more glucose that is stuck
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to them, the more you can infer that the average concentration of glucose is higher during the
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period of a red blood cells life. But of course, this is potentially misleading because if a red blood
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cell has a very short life, for example, I see this in a couple of my patients, including a patient
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who's recovering from prostate cancer, who still has some GI bleeding issues, patients with gastritis,
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et cetera, but women with a heavy menstrual period. So people who are losing significant amounts of
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blood have a higher turnover red blood cells. They're going to have an artificially low hemoglobin
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A1C. Conversely, people who have red blood cells that stick around a very long time, people with a
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microcytic pattern, meaning they have very small red blood cells that are less likely to get chewed up
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in the splenic system, which is where we ultimately break down red blood cells. They're going to have an
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artificially elevated hemoglobin A1C because their red blood cells are living longer on average than the
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typical person, which is about 90 days. So that's one reason why I'm not a huge fan of hemoglobin
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A1C. But the broader point here is that I find it unhelpful to simply say if your hemoglobin A1C is
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above 6.5 and you have type 2 diabetes, you have quote unquote a disease. If it is below 6.5, you are
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normal. Or even if we go one step further and say, well, there's a pre-diabetes, which is defined as
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5.7 to 6.4. And those people we have to watch out for, but anybody at 5.6 and down is completely normal
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as though there's some enormous difference between 5.6 and 5.7 or 6.4 and 6.5. So while on the one hand,
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I understand the need to simplify things, I think oversimplification is erroneous. And I think we
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should view these as a continuum. So glucose at the average level is a continuum. And as the person
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who asked the question noted, I am a far greater proponent of CGM. Now, Bob, I don't know if you're
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wondering what this thing on my arm is, but in case you are, this is a CGM. This is a continuous
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glucose monitor. And as its name suggests, it measures glucose continuously. And while I do not have
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diabetes, and while most of my patients don't have diabetes, many of them, along with I, wear this
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device. And I think what we'll get into today is the why. So what are the metrics we're tracking here?
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And what are we describing as ideal and optimal as opposed to acceptable along those metrics?
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Anything else I can say broad strokes before we jump into the nuts and bolts of this, Bob?
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No, I think that covers that other question about the continuous glucose monitor. Wondering if it's
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like streaming? Or does it actually take measurements every certain period of time?
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Yeah. So actually, I thought it would be helpful, Bob, to just sort of show you and obviously listeners
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kind of what this looks like. So it connects to your phone. And every five minutes, it is spitting out
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a number. If you look at it in a 24-hour fashion, when you turn your phone on your side,
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you get sort of the 24-hour tracing. So for my last 24 hours, I've averaged about 90 milligrams
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per deciliter. And my variability has been about 9 or 10 milligrams per deciliter, or my standard
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deviation. My peak level has been, let me see, I have to go back and look. My peak was 102. And by
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extension, then I've had no peaks above 140. That's going to come up later on. So obviously, if my peak
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was 102, I was never above 140. And my nadir was 77. So range of 77 to 102. So anyway, that's the kind
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of data you get out of these things. And obviously, they have reports that will spit out your average
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blood glucose over one day, seven day, 14 days, 30 days, 60 days, 90 days, et cetera, along with the
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standard deviation and things like that. And the way these things work, of course, is they're not
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actually measuring in the blood. They're measuring in the interstitial fluid. And that, of course,
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is the remarkable technology, right? It's that it's able to impute what the glucose level is
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in the blood without actually having to sample the blood. That's the magic of these things.
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Knowing you, I suspect I already know the answer, but I'll ask it anyway. Have you looked at your CGM
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and compared, say, like your three-month data to your HbA1c?
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Yeah. And there's no comparison. So because I actually have something called beta thalassemia
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minor, or I carry the trait for beta thalassemia, I have tiny little red blood cells, or as my
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roommate in med school, Matt McCormick, used to call it, shite for blood. The size of my red blood
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cells is very small. So my mean corpuscular volume and mean corpuscular hematocrit are very low.
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I'm not anemic because I make up for it by having a lot of them. So I have a lot of red blood cells.
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They're just all very small. So I have normal hemoglobin hematocrit oxygen carrying capacity,
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but my hemoglobin A1c always runs high. I've measured it as high as 5.8. The lowest I've
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ever had measured is 5.1. But anytime I've measured it, because I've been wearing CGM for almost six
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years now, if I go and check my A1c versus my trailing 90-day CGM, it almost always suggests
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that the hemoglobin A1c is higher by 0.5 to 0.8. If I measure a 5.7 on the hemoglobin A1c,
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it's overstating my blood glucose and it should really be about a 5.1 or a 5.2. And we see the
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opposite in some people. We have some patients where their CGM is actually showing us a much
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higher level of average blood glucose than what their hemoglobin A1c predicts. So it's important
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to understand hemoglobin A1c is a measurement that predicts average blood glucose. CGM actually gives you
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average blood glucose and you can reverse engineer an imputed A1c. It's obviously the latter that is
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much more interesting because you're directly measuring the variable of interest. Yeah, it's
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amazing. I mean, it's amazing technology. It's the difference between like a snapshot and a movie.
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Yeah, entirely. And from when I started wearing these things nearly six years ago, I thought,
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I don't know why everyone in the world isn't wearing it, notwithstanding the cost and the logistics
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of it. And the obvious reason why everyone wasn't wearing it was their cost prohibitive. And
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certainly back then they were quite involved, but they're getting better and better and better.
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And I'd like to believe that there will be a day when you go to your first visit at your doctor
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or prior to your first visit with your doctor, they mail you a CGM and you wear it for 30 days.
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And that data is looked at by your doctor and your doctor. By the time you arrive in the office,
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he or she has that information. And instead of looking at an A1c or a fasting glucose, they can really
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look at what your glucose excursions have looked like over a period of time in the real world.
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They do that with, I guess if things look fishy, but they'll do it with sphygmomanometer. They'll
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send you home with a blood pressure monitor and you'll take it every so often, maybe three times
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a day or whatever it is to get a look at your blood pressure that way.
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Right. We do that with our patients. Most of our patients, there's a particular blood pressure monitor
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we fancy and we have them keep it at home. We have a special log. We have a method that we want
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them to go about doing it, recording it, and we'll track that as well. Unfortunately in the wearable
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space, blood pressure is still far from prime time. We've tried a bunch of the wearables in that
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space and have not been impressed yet. I think there's going to be wearables in the blood pressure
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space soon. Okay. So where do you want to start on this? Because I know that part of the question
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that was posed is what are the metrics that we track? I want to go back and state my thesis,
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right? Or call it my hypothesis, I guess. My hypothesis is that outside of the formal diagnosis
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of type two diabetes. So now I'm referring to what the person asking the question called as
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quote unquote normal people. It's important that we leave quotes on that because I'm going to argue
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that that term has no meaning. But in the non-diabetic, which may be a better way to describe it,
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what is my argument? My argument is the following. Lower average blood glucose is better. A hemoglobin
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A1C of 5.1 is better than a hemoglobin A1C of 5.5, even though neither of those people are anywhere
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near having type two diabetes. Two, the more you can minimize glucose variability, the better. And of
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course, glucose variability is very difficult to measure without a CGM. Using a CGM, the standard
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deviation is the obvious mathematical tool to do that. And lower is better. Stated another way,
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if you have two people who both have an average glucose of 100 milligrams per deciliter, which by
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the way, corresponds to about a hemoglobin A1C of 5 to 5.1, which would be excellent. And one of them
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has a standard deviation of 10 milligrams per deciliter. And the other one has a standard deviation
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of 20, the person with the lower one is better off. Third, minimizing glucose peaks is important,
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irrespective of the first two things I said, average glucose and variability. Although obviously,
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the more peaks you have, it's going to all things equal push up glucose, and it will certainly increase
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variability. But I would argue specifically that glucose peaks are problematic and that we want to
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minimize them. Getting a little bit ahead of myself, what are the three metrics we are constantly
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tracking in our patients? And what am I constantly tracking in myself?
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