June 14, 2021

Nutritional biochemistry

#165 – AMA #24: Deep dive into blood glucose: why it matters, important metrics to track, and superior insights from a CGM

“Hyperinsulinemia on an [oral glucose tolerance test], even in the presence of normoglycemia, is the canary in the coal mine.” —Peter Attia

Read Time 32 minutes

In this “Ask Me Anything” (AMA) episode, Peter and Bob dive deep into blood glucose and why it matters so much with respect to metabolic health and longevity. They explain the need to pay close attention to metrics like average blood glucose, glucose variability, and peak glucose numbers. Additionally, Peter explains why he encourages all his patients, even nondiabetics, to utilize a continuous glucose monitor (CGM) which gives important insights that traditional lab testing and metrics consistently miss. 

If you’re not a subscriber and listening on a podcast player, you’ll only be able to hear a preview of the AMA. If you’re a subscriber, you can now listen to this full episode on your private RSS feed or on our website at the AMA #24 show notes page. If you are not a subscriber, you can learn more about the subscriber benefits here.

We discuss:

  • The problem with traditional blood tests and metrics for determining metabolic health [1:10];
  • The superior insights from a continuous glucose monitor [6:15];
  • Why lower is better than higher: average glucose, glucose variability, and glucose peaks [12:00];
  • Deep dive into average blood glucose and the importance of having the lowest average blood glucose possible [14:45];
  • Deep dive into glucose variability and why less variability is better [33:15];
  • Example of how HbA1c and traditional measures could catch metabolic issues too late [41:45];
  • Postprandial dips in blood glucose as a predictor of subsequent hunger and energy intake [43:00];
  • Exploring the idea that the suppression of fatty acids is actually causing hunger rather than a low blood glucose [49:45];
  • Deep dive into peak glucose and why lower peaks is better [57:15];
  • What the best rodent models tell us about the impact of peak glucose levels [1:06:25];
  • Why Peter encourages all his patients to wear a CGM [1:14:30]; and
  • More.

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*WATCH A PREVIEW OF AMA #24 ON YOUTUBE*

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The problem with traditional blood tests and metrics for determining metabolic health [1:10]

In terms of testing, Peter favors the oral glucose tolerance test (OGTT) over things like fasting glucose and HbA1c measurements as those can be misleading

Some great questions from subscribers:

  • “With insulin measurements. And also wearing a CGM to get a better sense of glucose homeostasis. My understanding is that OGTTs and CGMs are typically reserved for people with diabetes.”
  • Why does Peter find these tests useful in “healthy” people? 
  • What is Peter looking for when assessing someone’s glucose levels? 
  • What does he like and hate to see? 
  • How does Peter define normal versus abnormal control of glucose? 
  • If I’m not diabetic, do I have anything to worry about here?”
  • “Are you able to do a breakdown on what you look for on different people’s CGM data and what you would advise to improve their numbers similar to the AMA you did on lab tests?”

Peter’s reaction to these questions

  • These are the perfect questions, the most salient questions, the most important questions and this might become by extension one of the most important AMAs we do in terms of the aggregate impact it could have on health and longevity.”

“The mainstream medical system is just so out of sync with what I believe the future of medicine is going to be.” —Peter Attia

Defining T2D and HbA1c

  • Type 2 diabetes is defined as having a hemoglobin A1c (HbA1c) concentration greater than 6.5% which it corresponds to an average glucose of approximately 130 milligrams per deciliter
  • The way it HbA1c works is it measures the concentration of glycosylated hemoglobin. So it’s taking out red blood cells and it’s looking at how much glucose is stuck to them
  • the more glucose that is stuck to them, the more you can infer that the average concentration of glucose is higher during the period of a red blood cell’s life

This is potentially misleading in both directions because, for example: 

  • if a red blood cell has a very short life — GI bleeding issues, patients with gastritis, et cetera, women with a heavy menstrual period — So people who are losing significant amounts of blood have a higher turnover of red blood cells, they’re going to have an artificially low hemoglobin A1c
  • Conversely, people who have red blood cells that stick around a very long time — people with a microcytic pattern, meaning they have very small red blood cells that are less likely to get chewed up in the splanchnic system which is where we ultimately break down red blood cells — they’re going to have an artificially elevated hemoglobin A1c because their red blood cells are living longer on average than the typical person which is about 90 days

*The broader point:

  • Unhelpful to simply say if your hemoglobin A1c is above 6.5 and you have type 2 diabetes, you have “a disease”
  • If it is below 6.5 you are normal or even if we go one step further and say well there’s a pre diabetes which is defined as 5.7 to 6.4 and those people we have to watch out for
  • but anybody at 5.6 and down is completely normal
  • As though there’s some enormous difference between 5.6 and 5.7 or 6.4 and 6.5
  • on the one hand I understand the need to simplify things, I think oversimplification is erroneous and I think we should view these as a continuum
  • glucose at the average level is a continuum
  • “I am a far greater proponent of [continuous glucose monitors] (CGM)”

The superior insights from a continuous glucose monitor [6:15]

Continuous glucose monitor (CGM)

  • As its name suggests, it measures glucose continuously
  • “And while I do not have diabetes and while most of my patients don’t have diabetes, many of them along with I wear this device”
  • This podcast will get into the “why”

Using a CGM:

  • Your CGM connects to your phone and every five minutes it is spitting out a number in a 24 hour fashion
  • For Peter, his last 24 hours I’ve averaged about 90 milligrams per deciliter
  • His variability/standard deviation has been about 9 or 10 milligrams per deciliter
  • His peak level was 102, his nadir was 77 — So a range of 77 to 102
  • There are reports that will spit out your average glucose over 1 day, 7 days, 14 days, 30 days, 60 days, 90 days, etc. along with the standard deviation and more
  • A CGM is not actually measuring in the blood, it’s measuring in the interstitial fluid, a “remarkable technology”
  • It’s able to impute what the glucose level is in the blood without actually having to sample the blood

Bob asks Peter: Have you looked at your CGM and compared say like your three month data to your HbA1c?

  • Yes, “there’s no comparison” says Peter
  • Peter has something called beta thalassemia minor (or he carries the trait for beta thalassemia) which means he has tiny little red blood cells
    • His mean corpuscular volume and mean corpuscular hematocrit are very low, but he’s not anemic because his body makes up for it by having a lot of them
    • In result, he has a normal hemoglobin hematocrit oxygen carrying capacity but his hemoglobin A1c always runs high
    • He’s measured it as high as 5.8, and the lowest he’s ever measured is 5.1
    • After wearing a CGM for almost six years, if he goes back and checkers his A1c versus his trailing 90-day CGM, it almost always suggests that the hemoglobin A1c is higher by 0.5 to 0.8
    • If he measures a 5.7 on the hemoglobin A1c, it’s overstating his blood glucose and it should really be about a 5.1 or a 5.2
  • It’s also possible to see the opposite situation in some people where their CGM is actually showing a much higher level of average blood glucose than what their hemoglobin A1c predicts
  • So it’s important to understand hemoglobin A1c is a measurement that predicts average blood glucose
  • CGM actually gives you average blood glucose and you can reverse engineer and impute at A1c
  • The latter is much more interesting because you’re directly measuring the variable of interest

Peter’s hope for the future of medicine and doctor’s visits

  • The obvious reason why everyone isn’t wearing a CGM is that they are cost prohibitive and certainly back then they were quite involved
  • But they’re getting better and better and better, says Peter
  • “I’d like to believe that there will be a day when you go to your first visit at your doctor or prior to your first visit with your doctor they mail you a CGM and you wear it for 30 days and that data is looked at by your doctor and by the time you arrive in the office he or she has that information.”
  • “And instead of looking at an A1c or a fasting glucose they can really look at what your glucose excursions have looked like over a period of time in the real world.”

For example, some docs (including Peter) will do this for blood pressure (using a sphygmomanometer)

  • Peter says, “Most of our patients there’s a particular blood pressure monitor we fancy and we have them keep it at home. We have a special log.”
  • “Unfortunately in the wearable space blood pressure is still far from primetime”, says Peter, “but I think there’s going to be wearables in the blood pressure space soon”

Why lower is better than higher: average glucose, glucose variability, and glucose peaks [12:00]

Important CGM metrics 

For so-called “normal people” or the non-diabetic population, Peter would argue the following 3 points:

1 — lower average blood glucose is better

  • A hemoglobin A1c of 5.1 is better than a hemoglobin A1c of 5.5 even though neither of those people are anywhere near having type 2 diabetes

2 —  The more you can minimize glucose variability the better. 

  • And of course glucose variability is very difficult to measure without a CGM
  • Using a GGM, the standard deviation is the obvious mathematical tool to do that, and lower is better
  • For instance—
    • Say you have two people who both have an average glucose of 100 milligrams per deciliter (which corresponds to about an excellent HbA1c of 5.0 to 5.1) 
    • And one of them has a standard deviation of 10 milligrams per deciliter and the other one has a standard deviation of 20, the person with the lower one is better off.

3 — Minimizing glucose peaks is important (irrespective of the average glucose and variability)

  • Obviously the more peaks you have it’s going to, all things equal, push up glucose and it will certainly increase variability
  • But Peter would argue specifically that glucose peaks are problematic and that we want to minimize them

How Peter uses these metrics with patients:

{end of show notes preview}

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41 Comments

  1. A podcast discussing some of the lifestyle modifications that Peter has found useful and implemented for minimizing BG spikes and lowering average glucose would be helpful (I.e. post prandial walks, adding extra fat to a carb laden meal, etc.).

    Thanks!

  2. I was able to get a prescription for the Freestyle Libre 2 CGM via PushHealth in about 10 minutes for their $65 fee. The CGM is covered by insurance. I’ll let everyone know how it goes. If I could go back I would request one that reads direct to the phone. Apparently this method of reading is not FDA approved for Libre2 in the US, but works in the rest of the world.

    • I got my Freestyle Libre 2 from Walgreens after getting it prescribed on Push Health. I actually don’t mind the reader (vs. phone). It’s very small and light, and as long as you keep it near you, it reads the sensor continuously.

      I’ve learned some interesting things, for example, I’m going to face serious work to prevent my glucose from spiking to 140+ even with meals that I thought were not big hits of glucose, like Black Beans. My h1c was 5.0 at the last test.

      Unrelated, I was also able to get a Rx for Sirolimus (Rapamycin) on Push Health with no issues. My insurance wouldn’t cover it, but with a GoodRX coupon it was $150 for 30x1mg. I plan to start on 4mg qw, so it’s not so bad. Push Health seems like the best option for those wanting to self prescribe/ self experiment. They can also order any lab you want, which your insurance would then cover.

  3. Dr. Attia: the EPIC Norfolk study ( Pfister et al., 2011) that you use as evidence that lower glucose is better even in nondiabetics seems to actually argue the opposite: that glycemia only becomes problematic in the prediabetic range. The associations of HbA1c with total, cardiovascular, and cancer mortality for HbA1c in any of the ranges ≤ 5.5 are all equivalent to the reference (HR =1.0, HbA1c 5-5.5) until you get to HbA1c ≥ 5.0, at which point they start to rise — but of course that’s essentially the cutoff for prediabetes (HbA1c = 5.6). And there are lots of prospective studies either similarly finding no associatons in the nondiabetic range, or even finding a J-shaped curve — for instance:
    http://dx.doi.org/10.1210/jc.2018-02536
    http://dx.doi.org/10.1056/NEJMoa0908359
    http://dx.doi.org/10.1210/js.2019-00033

  4. Following up on my comment about EPIC, above: the NIPPON DATA90 results (Sakurai et al., 2013) are even further from supporting a worry about people with HbA1cs in the normal range: they only found associations with all-cause or CVD mortality for HbA1c ≥6.0, which already significantly exceeds the prediabetic cutoff:they found no significant association even in the first half of the prediabetic distribution (5.5-5.9). Doesn’t this (and the EPIC data, and the other studies I cited finding a J-shaped curve) argue that there’s not likely any benefit to be gained from shooting for even tighter glucoregulation in normoglycemic people?

    • (I take your point about the problem of overadjusting for the metabolic sequelae of metabolic syndrome, but the analysis I gave above also holds when adjusting only for sex and age, which are not subject to this downstream confounder problem).

  5. I would love more information on variability of glucose levels and how to minimize them. After discordance between multiple fasting glucose measurements and A1C results, I’m currently in day 5 of using the Freestyle Libre and my variability is certainly larger than Dr. Attia’s, but I have no idea what variability is acceptable and how to lower the range.

  6. I have been wearing a CGM for about a year (Freestyle Libre) that reads to my phone. I have learned a lot from this tool and plan to continue to wear one. A couple of questions for Dr. Attia (or anyone who knows the answers):
    1. Do blood glucose spikes that result from exercise have the same detrimental effects at the mitochondrial level, and if so, how should I think about the trade-off between exercise benefits and blood glucose spikes?
    2. How can I easily view my blood glucose spikes and variability with data captured by the Freestyle Libre? I can easily view average blood glucose, but I seem to have to go day by day to view the spikes and variability.

    • I’d also like more insight into your question about exercise induced BG spikes. I wore the Freestyle Libre for 9 months and saw this quite frequently in high intensity exercise sessions.

    • Yes, I’d like to know about the blood glucose spikes during exercise with the freestyle Libre as well.

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