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Speaker 1: I'm a starver. Full disclosure, I am doing this podcast

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Speaker 1: on an empty stomach. I have had nothing to eat

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Speaker 1: yet today.

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Speaker 2: I think that's terrible. I literally have had three meals already.

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Speaker 3: It's that thirty in the morning.

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Speaker 2: I've had three meals, And what do you think your

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Speaker 2: glucose is?

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Speaker 1: Well? So people always have this idea that there's this like,

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Speaker 1: oh my glucose is low, I'm hypoglycemic, you know, and

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Speaker 1: that's why I'm angry or I'm confused or whatnot. But

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Speaker 1: the beautiful thing about the human body, assuming all physiology

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Speaker 1: is normal and you don't have diabetes and you're not

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Speaker 1: taking exogenous insulin, is that you've got plenty of glucose

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Speaker 1: going around. You have glycogen in your liver that is

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Speaker 1: stored and rapidly converted to glucose. And even when you

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Speaker 1: run out of glycogen, you have fat stores and things

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Speaker 1: like that that can be liberated to give you some glucose.

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Speaker 1: And so I feel great. And yes, I'm staring at

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Speaker 1: this box of thin mints that is right next to me,

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Speaker 1: just in case.

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Speaker 3: Just in case, I will say.

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Speaker 2: You did have to start the cold open twice, and

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Speaker 2: I think maybe you should take a gel or a

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Speaker 2: thin mint to kind of cut you out.

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Speaker 1: I think we should do the experiment the next time

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Speaker 1: I say something stupid, eat a thin mint. I'll eat

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Speaker 1: a thin min and then we'll see.

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Speaker 2: I'm Emily Aster, I'm an economist and a data expert.

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Speaker 1: And I'm Perry Wilson, I'm a medical doctor.

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Speaker 2: It's Thursday, April sixteenth, twenty twenty six. And this is

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Speaker 2: wellness actually.

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Speaker 1: Because you're getting a staggering amount of health and wellness

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Speaker 1: information nowadays from every source imaginable, and some of it

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Speaker 1: is awesome and.

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Speaker 2: Some of it is well actually both Fortunately we are

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Speaker 2: both people who know how to read studies, how to

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Speaker 2: parse the data, and can tell you what's worth thinking

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Speaker 2: about and what you can safely ignore.

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Speaker 1: But before we dig in, another that this podcast is

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Speaker 1: for educational purposes and should not be construed as medical advice.

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Speaker 1: We don't know your unique situation, so talk to your

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Speaker 1: doctor for personal health decisions.

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Speaker 2: This week, we're asking what's the deal with continuous glucose monitors?

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Speaker 2: Perry and I will give the official smasher pass, and

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Speaker 2: then we'll get to your question of the week. But

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Speaker 2: first let's do the health news roundup after the break, and.

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Speaker 1: We are back with the health news of the week. Emily,

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Speaker 1: I want to start with a really interesting article that

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Speaker 1: came to us from the New York Times showing that

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Speaker 1: birth rates are on the decline overall, but with some

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Speaker 1: nuance that I think you might be able to walk

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Speaker 1: us through. So can you tell me about what's going

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Speaker 1: on with birth rates?

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Speaker 2: Yeah, so globally birth rates are declining. This has gotten

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Speaker 2: a lot of discussion in the last few years. We

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Speaker 2: are seeing many countries with far below replacement, which includes

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Speaker 2: the US replacement fertilities about two point two births per woman.

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Speaker 2: And so the kind of headline finding on this is

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Speaker 2: that the fertility rate in twenty twenty five fell to

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Speaker 2: a new low, and that is being driven largely by

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Speaker 2: very big declines in fertility among younger people. So the

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Speaker 2: teen birth rate has like totally cratered in the past

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Speaker 2: twenty years, which I just want to be clear, is good,

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Speaker 2: Like it's good. Fifteen to nineteen year olds broadly is

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Speaker 2: not the group you want to be having the most babies.

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Speaker 2: And so what we've seen is, for you know, younger cohorts,

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Speaker 2: there's a cratering of the fertility rate, and then for

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Speaker 2: older cohorts it's going up.

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Speaker 3: So births among forty.

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Speaker 2: Five to fifty four year olds have increased about eighty

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Speaker 2: five percent eighty three percent since two thousand seven, so

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Speaker 2: we're kind of seeing a shift, and that's making it

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Speaker 2: a little hard to know exactly where, like completed fertility

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Speaker 2: will end up. So at the end of people's lives,

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Speaker 2: will they actually have fewer kids. I think the truth

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Speaker 2: is they will end up having fewer kids because if

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Speaker 2: you wait until you're forty five to start having children,

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Speaker 2: you're not going to have as many children. It's going

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Speaker 2: to be harder. So there's a lot of complexities here.

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Speaker 1: Can I just tell you, I don't know if I've

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Speaker 1: ever told you that I have three recurring nightmares, like

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Speaker 1: three genres.

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Speaker 3: Hey, we're going to say three children and then but

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Speaker 3: maybe it's the same. I don't know.

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Speaker 1: One is about the first kid. Okay, my three recurring nightmares.

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Speaker 1: One I have the thing where like your teeth are

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Speaker 1: falling out, like that very uncomfortable thing. Two We're in

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Speaker 1: college and it's final exam and I realize I have

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Speaker 1: not gone to class all year, and I have no

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Speaker 1: idea what's going on? Which is kind of accurate from

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Speaker 1: my college experience. And then my third is that I'm

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Speaker 1: having another baby. I'm forty six years old and my

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Speaker 1: wife tells me like, oh, We're having another baby. And

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Speaker 1: I can just say, for very good surgical reasons, this

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Speaker 1: is not possible for me, thankfully, but I still have

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Speaker 1: that dream. So forty five to fifty four year olds

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Speaker 1: having some babies. Good for you, I salute you. It

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Speaker 1: terrifies me at this point.

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Speaker 2: Yeah, same here, but but I think, I mean, it's

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Speaker 2: a very interesting demographic shift, and we are going to

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Speaker 2: have to wait and see where this ends up. But

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Speaker 2: my instinct is will end up with your kids overall,

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Speaker 2: even if it is maybe not as many fewer as

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Speaker 2: you would have anticipated without taking into account the movement

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Speaker 2: in time, Okay, shifting gears significantly. Last week, there was

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Speaker 2: a report that the CDC, and in particular Jay Boticharia,

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Speaker 2: the head of the both the CDC and the NIH,

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Speaker 2: has delayed a publication in the Mortality and Morbidity Weekly Report,

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Speaker 2: which is a CDC publication.

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Speaker 3: He's delayed a report.

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Speaker 2: That was calculating COVID vaccine effectiveness against hospitalization, and this

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Speaker 2: got some attention as potentially politically motivated. He said it

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Speaker 2: was because he doesn't like the methods.

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Speaker 3: What do you think?

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Speaker 1: Okay, Well, so the Morbidium Metality Weekly report by the

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Speaker 1: CDC something we've referenced before on this podcast. It's a

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Speaker 1: wonderful source for sort of up to the minute, or

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Speaker 1: at least the past month disease epidemiology in the US

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Speaker 1: and sometimes abroad. And they will periodically report on vaccine effectiveness,

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Speaker 1: which is like, how good was last year's vaccine? We

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Speaker 1: talked on a prior episode that last year's flu vaccine,

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Speaker 1: for example, not a great one. We didn't have a

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Speaker 1: great flu vaccine. It was like thirty percent effective at

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Speaker 1: present preventing hospitalizations. This report showed that last year's COVID

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Speaker 1: vaccine was about fifty percent effective at venting hospitalizations, which means,

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Speaker 1: you know, all else being equal, that you were half

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Speaker 1: as likely enough up in the hospital if you had

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Speaker 1: been vaccinated as if you hadn't been vaccinated. That report

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Speaker 1: passed the Scientific Review Group at the CDC, but, according

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Speaker 1: to the Washington Post, was held up by doctor Bodicharia

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Speaker 1: for quote methodologic concerns. I mean the thing about that.

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Speaker 1: So this methodology is called a test negative case control design.

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Speaker 1: It basically looks at people who have infections that show

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Speaker 1: up with infections that look like COVID, like the respiratory infections,

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Speaker 1: So it could be flu, it could be a bad cold,

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Speaker 1: could be COVID. Who knows. They get tested and they

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Speaker 1: assess their vaccination status, and you basically find that, oh,

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Speaker 1: if you are vaccinated for COVID, that thing you showed

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Speaker 1: up with, that respiratory infection you showed up with is

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Speaker 1: much less likely to be COVID than if you weren't

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Speaker 1: vaccinated with COVID. That's how it works. This design has

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Speaker 1: been used for for years and.

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Speaker 3: Years and years, very common zine, very.

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Speaker 1: Common design, and even was recently in the Morbiditing Immortality

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Speaker 1: Weekly report when they reported on flu vaccine effectiveness. And

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Speaker 1: theoretically it's the exact same analysis. So I don't see

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Speaker 1: why it's like bad for the COVID vaccine, but it's

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Speaker 1: not bad for other vaccines.

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Speaker 2: I find this decision so interesting because you're totally right.

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Speaker 2: This method is used all the time, and so this

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Speaker 2: singling out the COVID vaccine and saying we're going to

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Speaker 2: pull this report for COVID when we just published the

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Speaker 2: same exact thing for flu. Seems totally politically motivated, particularly

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Speaker 2: coming out of an administration that has generally been very

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Speaker 2: skeptical of the COVID vaccine, even though they did develop.

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Speaker 1: It develop it.

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Speaker 2: On the other hand, I think this method is very

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Speaker 2: poor and I share like I come out of the

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Speaker 2: same intellectual tradition as Jay, and I share a lot

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Speaker 2: of his concerns about this method, and so I'm sort

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Speaker 2: of very interested in what was behind his decision. And

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Speaker 2: I will just like shameless plug. I am interviewing Jay

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Speaker 2: on April twenty fourth at a National Academy's panel. We

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Speaker 2: can put the registration link in the show notes, and

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Speaker 2: I'm going to ask him about this exact Please ask him.

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Speaker 1: Yeah, it is. It is not a perfect design. It's

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Speaker 1: not a randomized trial. Obviously, we don't do randomized trials

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Speaker 1: once we confirm that a drug or vaccine works because

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Speaker 1: it's unethical to randomize people to place ebo. There are

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Speaker 1: other methods to assess vaccine effectiveness, but as you point out,

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Speaker 1: this one has the one that we use. It's the

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Speaker 1: one that we use. So I look forward to your

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Speaker 1: hard hitting questions for doctor j.

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Speaker 2: Okay I'm just going to call him that because that's

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Speaker 2: going to start an interview with somebody.

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Speaker 1: Turning now to the great state of California. Laws in

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Speaker 1: California tend to have the effect of like propagating across

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Speaker 1: the US because it's such a huge market, and California

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Speaker 1: is soon to be mandating that their corn tortillas get

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Speaker 1: supplemented with folic acids. So, Emily, as you know, folate

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Speaker 1: is one of the supplements that gets added to like

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Speaker 1: wheat flour and stuff like that all across the country.

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Speaker 1: There's some important public health implications, but it was actually

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Speaker 1: news to me that the corn meal and corn flour

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Speaker 1: that is used for tortillas it doesn't have folate added. So, Emily,

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Speaker 1: I assume you're a fan of folate.

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Speaker 3: I am a fan of folate.

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Speaker 4: Yeah.

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Speaker 3: I mean, when we look at prenatal vitamins, a.

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Speaker 2: Lot of the stuff they put in there is not useful,

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Speaker 2: but fullic acid or folate is in fact a very

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Speaker 2: well established evidence based way to prevent neural to defects,

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Speaker 2: and not everybody gets prenatal vitamins at the range that

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Speaker 2: they should, so it's very important that people get folate

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Speaker 2: or folic acid in food, and in particular in Hispanic communities,

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Speaker 2: corn tortillas. Corn meal is a more common source of

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Speaker 2: carbohydrates than flour, and so I think this is a

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Speaker 2: directionally good idea to put this in corn. So I'm

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Speaker 2: a fan of this. RFK was not a fan, but

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Speaker 2: he hasn't exactly said why. He said it was insanity,

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Speaker 2: but I wasn't totally.

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Speaker 3: Clear vanity and it is targeting.

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Speaker 2: The poor, which is actually true, but like in a

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Speaker 2: positive way. So I'm not really I'm not totally sure

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Speaker 2: what the objection is to this particular, Like who could

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Speaker 2: object to this, I'm not except that the tortilla makers

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Speaker 2: who have to add something which costs money.

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Speaker 3: But other than that, yeah, I don't Yeah.

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Speaker 1: Yeah, Okay, that's strange case. Opresa from RFK.

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Speaker 3: Wow, that is that is.

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Speaker 2: I hope that none of the college classes that you're

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Speaker 2: dreaming about were in Spanish, Verry, because it's.

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Speaker 1: Not my best language. Maybe it is time for a

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Speaker 1: thin mint. That's it for the health news of the week.

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Speaker 1: After the break, what's the deal with continuous glucose monitors?

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Speaker 1: And we're back to talk about what's the deal with

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Speaker 1: continuous glucose monitors? To start us off, Emily, I'm going

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Speaker 1: to play you a clip of our friend Casey means

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Speaker 1: she's been mentioned multiple times on this podcast, perennial Surgeon

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Speaker 1: General candidate. Still I think nominee Casey means talking to

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Speaker 1: Andrew Huberman about continuous glucose monitors.

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Speaker 5: The purpose of the glucose monitor is curiosity, essentially an

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Speaker 5: MRI for how all of our different dietarian lifestyle strategies

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Speaker 5: are creating this readout of glucose in our body, which

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Speaker 5: I think can be really interesting.

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Speaker 1: So, Emily, cgms, simply put, are an MRI of glucose.

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Speaker 3: No, that's not I don't know what does that mean.

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Speaker 1: It's just it's an MRI of glucose. Mrizer fancy and

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Speaker 1: complicated and is a.

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Speaker 3: Giant machine Perry which image is.

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Speaker 1: It's okay, I thought you'd like it.

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Speaker 3: I didn't like it.

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Speaker 1: I you know, continuous glucose monitors are things that measure

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Speaker 1: glucose and we're going to talk a little bit about

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Speaker 1: how they do that and why you might want that.

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Speaker 1: But I thought it would be good to play that

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Speaker 1: clip right off the bat, because it sets the tone

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Speaker 1: for what we see and a ton of influencer marketing

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Speaker 1: and social media. You've got the music in the background,

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Speaker 1: You've got you know, these sort of medical and scientific

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Speaker 1: terms like MRI getting thrown about, and yet contextually I'm

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Speaker 1: not entirely sure what she's saying, right, Like, is there

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Speaker 1: a statement of fact there that we can hang our

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Speaker 1: hat on and ask for some evidentiary support.

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Speaker 2: What I think is interesting about that statement I think

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Speaker 2: we're going to come back to is she starts by

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Speaker 2: saying like continuous glugo monitors, but she uses.

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Speaker 3: The word curiosity.

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Speaker 2: Yeah. Yeah, I actually think that is a big piece

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Speaker 2: of why many people outside of diabetics, of course talk

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Speaker 2: about that case. But that's that's a reason that many

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Speaker 2: people use this. And one of the things I think

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Speaker 2: I'm going to push a little bit more later is

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Speaker 2: like this, like with any of these sort of self

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Speaker 2: tracking things, should be in the service of a decision,

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Speaker 2: probably not just in the service of curiosity. And so

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Speaker 2: it should be in the service of a decision or

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Speaker 2: an action, because this isn't maybe something you need to

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Speaker 2: be curious about unless you were going to use it

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Speaker 2: for something.

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Speaker 3: But we can get into that more.

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Speaker 2: I will say I was at a race a couple

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Speaker 2: of weeks ago and there were a bunch of people.

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Speaker 3: A lot of athletes are wearing these on their arms.

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Speaker 1: I'm seeing them more and more.

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Speaker 3: I see them a lot.

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Speaker 1: Yeah, I was on vacation last week and so I

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Speaker 1: saw them like by the pool. And you know, as

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Speaker 1: a physician, I've seen these for a long time, but

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Speaker 1: typically just in patience with diabetes. I mean originally just

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Speaker 1: in patience with type one diabetes. And then you know,

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Speaker 1: you can see how this market has grown and for

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Speaker 1: people who might not be as familiar. Continuous glucose monitors

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Speaker 1: are these patches that you can stick on your body.

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Speaker 1: Typically they'll go on the back of the arm or

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Speaker 1: sometimes the belly. They have a small wire in them

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Speaker 1: that sticks into the skin and can sense the glucose

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Speaker 1: content of this what's called the interstitial fluid, So it's

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Speaker 1: not sitting in your blood per se, but it's kind

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Speaker 1: of like the fluid that kind of percolates throughout your body.

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Speaker 1: And these used to be prescription devices that your doctor

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Speaker 1: would write your prescription for if you had diabetes to

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Speaker 1: help monitor your blood sugar. These were really a godsend

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Speaker 1: for people that were doing fingersticks frequently because finger sticks

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Speaker 1: hurt and it's annoying. You're carrying around all these needles

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Speaker 1: and stuff like that. So you know, you just stick

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Speaker 1: this thing on it measures it through an app. But

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Speaker 1: as of I think twenty twenty four, the FDA had

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Speaker 1: approved a over the counter version, which you know anyone

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Speaker 1: can use, and that caused this explosion in the wellness

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Speaker 1: base of people without diabetes using these devices for all

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Speaker 1: sorts of purposes that we'll get into.

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Speaker 2: Yeah, So I actually think it's worth backing up just

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Speaker 2: one more step into talking about why you might care

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Speaker 2: about your glucose and how glucose is sort of operating

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Speaker 2: in the body. So I'm going to ask you, because

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Speaker 2: you're the doctor, when I eat, it turns into glucose,

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Speaker 2: and why would I care how much of that there is?

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Speaker 1: Yeah, Glucose is the sort of energy currency of the body. Basically, right,

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Speaker 1: you put things into your body and buying large proteins

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Speaker 1: and carbohydrates are going to turn into glucose. Fats can

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Speaker 1: also turn into glucose sort of eventually, although it takes

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Speaker 1: a few more steps and your cells literally burn glucose

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Speaker 1: to make energy. It takes a glucose molecule and it

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Speaker 1: turns it into carbon dioxide and water. If you ever

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Speaker 1: remember from like sixth grade science class, you could take

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Speaker 1: some sugar and you know, you add chemicals and it

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Speaker 1: like off gas is carbon dioxide and things like that. That's

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Speaker 1: the same stuff that's happening in your self.

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Speaker 2: Science class is much better than mine.

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Speaker 1: And so yeah, I mean you need glucose to survive.

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Speaker 1: I think what's relevant to continuous glucose monitors is that

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Speaker 1: glucose is is kept in a very narrow range in

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Speaker 1: your blood because glucose itself can be toxic. So it's necessary,

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Speaker 1: but it's also sort of evil. And things that are

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Speaker 1: necessary and yet evil tend to be regulated in like

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Speaker 1: very narrow bands in the human body. And it's insulin

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Speaker 1: that does that. And so your blood sugar, for example,

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Speaker 1: which you know, normal blood sugar, let's say, is ninety

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Speaker 1: miligrams per test leader or something that's corresponds to about

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Speaker 1: a teaspoons worth of sugar being dissolved in your blood

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Speaker 1: at any given time. So that's not much, right, Like

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Speaker 1: put a teaspoon of sugar in my coffee this morning,

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Speaker 1: that's all the sugar in your blood, which should tell

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Speaker 1: you how much is going on in your body to

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Speaker 1: pull it out of your blood and put it into

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Speaker 1: cells and put it into storage and make glycogen, which

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Speaker 1: sits in the liver is like a warehouse for glucose

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Speaker 1: if you need it in the future. And so it's

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Speaker 1: highly highly regulated, and when it becomes disregulated, that's what

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Speaker 1: we call diabetes, and obviously that's one of the major

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Speaker 1: chronic health conditions of our time.

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Speaker 2: Great, Okay, So the idea behind these cgms is that

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Speaker 2: they will tell you in more real time what your

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Speaker 2: blood sugar is. And let's sort of get the diabetes

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Speaker 2: piece of this out of the way, because I think

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Speaker 2: this is a place where it's very clear this innovation

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Speaker 2: has made huge strides for.

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Speaker 3: People living with diabetes.

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Speaker 2: So one of the core issues, whether you have type

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Speaker 2: one or type two diabetes is monitoring where your blood

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Speaker 2: sugar is so you can both think about which you

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Speaker 2: know foods cause your blood sugar to rise above levels

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Speaker 2: that are safe, and so you can make sure your

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Speaker 2: blood sugar doesn't fall too low. And for people with

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Speaker 2: type one diabetes, they need to dose with insulin or

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Speaker 2: possibly get more sugar if their blood sugar drops too low.

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Speaker 2: Before continuous monitoring you're taking fingersticks very frequently. That is

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Speaker 2: both unpleasant, time consuming, and also subject to quite a

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Speaker 2: lot of noise because of course you're not continuously pricking

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Speaker 2: your finger. This is effectively a way to be continuously

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Speaker 2: pricking your finger without the pain and discomfort of doing so.

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Speaker 2: And so we have a lot of evidence that you know,

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Speaker 2: for type one diabetes, this is clearly very beneficial and

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Speaker 2: especially for kids, lowers the risks of hypoglycemia, which is

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Speaker 2: in blood sugar drops too low. It improves anxiety in parents.

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Speaker 2: It's a kind of follow on, but we just have

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Speaker 2: a lot of evidence type two and type one diabetes

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Speaker 2: from RCTs that people are spending more time in the

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Speaker 2: appropriate range, that their A one c's are going down.

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Speaker 2: Like all of the metrics you would have for successfully

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Speaker 2: managed diabetes are improved with this kind of monitoring for

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Speaker 2: reasons that seem totally obvious to me.

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Speaker 1: Yeah, I mean, it's it is a slam dunk for

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Speaker 1: type one diabetics. It is a pretty darn close for

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Speaker 1: type two diabetics. One of the cool things for people

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Speaker 1: type one diabetes is that some of the prescription continues

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Speaker 1: glucose monitors actually integrate with insulin pumps, and so not

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Speaker 1: only is it measuring the glucose in your you know,

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Speaker 1: interstitial fluid, which is close to your blood. It lags

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Speaker 1: behind the blood by about ten minutes, but it's getting

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Speaker 1: relatively updated glucose measurements. But it can also control how

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Speaker 1: much insulin is being released by the pump, which is

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Speaker 1: it's like you put those two things together and you

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Speaker 1: basically have a pancreas. I mean it's not perfect, but

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Speaker 1: like that's more or less what the pancreas is doing,

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Speaker 1: and it's been a total game changer, you know.

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Speaker 2: I think the other piece of this is in people

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Speaker 2: with type two diabetes, even if they are not insulin dependent,

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Speaker 2: or even people with pre diabetes, there is some evidence

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Speaker 2: that this can help people basically manage their food intake

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Speaker 2: in a way that keeps their blood sugar more Even

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Speaker 2: because you're getting a continuous set of feedback and different

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Speaker 2: foods raise people's blood sugar different amounts, there's a kind

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Speaker 2: of general sense that like refined carbohydrates, like those thin

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Speaker 2: mints that you're definitely going to eat happy through this episode,

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Speaker 2: that those raise your blood glucose quite fast and sort

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Speaker 2: of slow processing carbohydrates or protein raise it more slowly,

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Speaker 2: but that's not true for every individual in exactly the

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Speaker 2: same way. And so these are a way for people

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Speaker 2: to kind of both get real time feedback but also

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Speaker 2: figure out kind of what works for them. And there's

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Speaker 2: some evidence that can help people keep their blood sugar

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Speaker 2: in the range that you're looking for, which is something

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Speaker 2: like sort of seventy two one hundred and forty is

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Speaker 2: the range people are typically looking for over the course

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Speaker 2: of a.

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Speaker 1: Day, right, And for people with pre diabetes, cgms have

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Speaker 1: been shown in some studies at least to delay the

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Speaker 1: progression or even prevent the progression into full blown diabetes,

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00:22:02,440 --> 00:22:06,120
Speaker 1: which is obviously an important outcome. And so for these

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Speaker 1: people with disorders of insulin metabolism. Right, So, from pre

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Speaker 1: diabetes through diabetes, there's something disordered about this highly regulated system,

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00:22:17,480 --> 00:22:21,040
Speaker 1: where a window into how that system is working can

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Speaker 1: help you engage in behavioral changes to keep things working

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Speaker 1: well for longer. But where the rubber meets the road then,

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Speaker 1: is like, what about all the people for whom the

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Speaker 1: system is already working as intended? Is their marginal benefit

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Speaker 1: there and the vast majority of people who are using

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Speaker 1: these over the counter continuous glucose monitors are really those types.

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Speaker 1: So you know, I think that what people really want

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Speaker 1: to know is like, Okay, I'm not diabetic. I don't

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Speaker 1: have pre diabetes, I don't have you know, the metabolic syndrome,

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Speaker 1: which is a risk factor for diabetes. But I see

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Speaker 1: these things out there, and there's influencers online telling me

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Speaker 1: that like, I need to use this to lose weight.

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Speaker 1: I need to lose this to get my muscle's better.

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Speaker 2: I need to I think a lot of the messaging

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Speaker 2: is even much more specific than that. It's not like

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Speaker 2: this is going to help you lose weight or get

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Speaker 2: your muscles better, although that's out there. It's just like,

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Speaker 2: you should use this to keep your glucose in a

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Speaker 2: in a rate, like you should use this to not

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Speaker 2: have glucose spikes and to sort.

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Speaker 3: Of do various things to make your glucose.

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Speaker 2: Like there's this idea that like the optimal glucose is

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Speaker 2: a flat line at ninety like all the time somehow,

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Speaker 2: like and we're all we should be like navigating our diets,

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Speaker 2: so our life is flat at ninety all the time.

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Speaker 1: Oh my gosh, yeah, so important. No, And actually, as

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Speaker 1: I was researching the study, and I was looking at

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Speaker 1: some of these things, you know, you see all these

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Speaker 1: people being like, I had no idea how much my

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Speaker 1: glucose spiked after I did X, Y or Z, And

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Speaker 1: I think there's even like the automoonopoeia of the word spike.

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00:23:52,480 --> 00:23:57,679
Speaker 1: It's just like that sounds bad, right, pointy sharp. We

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00:23:57,720 --> 00:24:01,760
Speaker 1: don't like spiking things, and so I but I wanted

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Speaker 1: to dig into literature a little bit just to like

439
00:24:03,920 --> 00:24:06,120
Speaker 1: check my priors and be like, wait, is that bad?

440
00:24:06,280 --> 00:24:09,120
Speaker 1: Like is it bad that you know, after you eat

441
00:24:09,680 --> 00:24:11,840
Speaker 1: something that has a lot of carbohydrates in it, the

442
00:24:11,840 --> 00:24:14,080
Speaker 1: glucose level in your blood goes up. And of course

443
00:24:14,119 --> 00:24:17,120
Speaker 1: it turns out that like, well, of course the glucose

444
00:24:17,160 --> 00:24:19,119
Speaker 1: level in your blood goes up when you eat carbohydrates,

445
00:24:19,160 --> 00:24:22,000
Speaker 1: because that's that's what they do. That's what they do,

446
00:24:22,520 --> 00:24:23,919
Speaker 1: and that's what you want them to do.

447
00:24:24,400 --> 00:24:26,320
Speaker 3: So four people who are thinking about this is just

448
00:24:26,359 --> 00:24:27,080
Speaker 3: like level set?

449
00:24:27,200 --> 00:24:31,000
Speaker 2: What is it that one acquires in doing this? So

450
00:24:31,080 --> 00:24:33,720
Speaker 2: you can go on the internet and you can purchase

451
00:24:33,960 --> 00:24:36,119
Speaker 2: from a couple of different companies.

452
00:24:36,480 --> 00:24:40,200
Speaker 3: Abb It makes one, xcom makes one. You can purchase

453
00:24:40,240 --> 00:24:41,360
Speaker 3: one of these monitors.

454
00:24:41,359 --> 00:24:43,600
Speaker 2: You can purchase it for like a long term subscription,

455
00:24:43,720 --> 00:24:45,600
Speaker 2: or you can actually buy them for like fifty bucks.

456
00:24:45,640 --> 00:24:47,600
Speaker 2: You can get this for two weeks and it's like

457
00:24:47,640 --> 00:24:51,879
Speaker 2: a two week single use monitor, and they ship it

458
00:24:51,920 --> 00:24:54,880
Speaker 2: to you and I, I've done this so I can

459
00:24:54,920 --> 00:24:57,960
Speaker 2: tell us, tell you what happens. They ship you a

460
00:24:58,000 --> 00:24:59,960
Speaker 2: like a little thing. You stick it in your ow.

461
00:25:00,920 --> 00:25:03,440
Speaker 3: It doesn't really hurt, like a tiny needle. It goes

462
00:25:03,480 --> 00:25:04,119
Speaker 3: into your arm.

463
00:25:04,160 --> 00:25:06,320
Speaker 2: It sticks on your arm, and it links up to

464
00:25:06,359 --> 00:25:10,080
Speaker 2: an app and the app will tell you in something

465
00:25:10,160 --> 00:25:13,640
Speaker 2: close to you know, realish time kind of how your

466
00:25:13,800 --> 00:25:17,240
Speaker 2: blood glucose is moving around, and that's it. Then you

467
00:25:17,320 --> 00:25:20,120
Speaker 2: like look at that app at different times and use

468
00:25:20,160 --> 00:25:22,960
Speaker 2: it for some kind of decision making and there's a

469
00:25:22,960 --> 00:25:25,280
Speaker 2: little line and that's that's it.

470
00:25:25,600 --> 00:25:28,840
Speaker 1: Okay. One of the problems we have in research of

471
00:25:28,880 --> 00:25:33,320
Speaker 1: where we have frequently sampled data. Take a hospitalized patient's

472
00:25:33,359 --> 00:25:36,120
Speaker 1: blood pressures. Okay, so we have a patient in the hospital.

473
00:25:36,280 --> 00:25:38,680
Speaker 1: They have blood pressure measured every fifteen minutes or something

474
00:25:38,680 --> 00:25:40,199
Speaker 1: if they're in the intensive carry unit. Right, So we

475
00:25:40,200 --> 00:25:42,959
Speaker 1: have all this data, we always have the question of

476
00:25:42,960 --> 00:25:46,000
Speaker 1: like what how do I collapse that into a meaningful number,

477
00:25:46,200 --> 00:25:48,359
Speaker 1: Like when it comes to a bunch of glucoses. Is

478
00:25:48,400 --> 00:25:50,679
Speaker 1: it the high that matters? Is it the low? Is

479
00:25:50,720 --> 00:25:54,400
Speaker 1: it the the variation? Right, like the coefficient of variation?

480
00:25:55,040 --> 00:25:57,639
Speaker 1: Are the apps breaking that down or is just like

481
00:25:57,680 --> 00:25:59,600
Speaker 1: here's your line and like it turns red when it's

482
00:25:59,600 --> 00:25:59,919
Speaker 1: above one.

483
00:26:01,119 --> 00:26:04,080
Speaker 3: Yeah, it's more of the second thing. I would say.

484
00:26:04,720 --> 00:26:08,160
Speaker 3: You know, they're not in in this use case.

485
00:26:08,480 --> 00:26:11,879
Speaker 2: They are generally not trying to provide people, I think,

486
00:26:12,040 --> 00:26:16,439
Speaker 2: with much beyond like here's how your glucose changes with

487
00:26:16,840 --> 00:26:19,080
Speaker 2: you know, around the day.

488
00:26:19,960 --> 00:26:20,960
Speaker 3: And I think it's.

489
00:26:20,840 --> 00:26:23,879
Speaker 2: Partly because the pitch people are getting is like you

490
00:26:23,920 --> 00:26:27,159
Speaker 2: can see which foods raise your glucose, so you can put.

491
00:26:27,040 --> 00:26:29,040
Speaker 3: In like your I did this activity, you know, I

492
00:26:29,240 --> 00:26:30,000
Speaker 3: ate this whatever.

493
00:26:30,040 --> 00:26:32,040
Speaker 2: So I think the idea is in principle you could

494
00:26:32,080 --> 00:26:36,080
Speaker 2: combine this with activity or food consumption tracking and then see,

495
00:26:36,160 --> 00:26:39,280
Speaker 2: you know, when I eat rice or when I eat cookies,

496
00:26:39,359 --> 00:26:42,840
Speaker 2: my glucose goes up more, and then when I eat

497
00:26:43,000 --> 00:26:45,080
Speaker 2: you know, eggs, it doesn't go up as much, And

498
00:26:45,080 --> 00:26:48,359
Speaker 2: that that would be in some way informative to again

499
00:26:48,520 --> 00:26:52,119
Speaker 2: achieve this goal that I don't think anyone health wise

500
00:26:52,160 --> 00:26:54,240
Speaker 2: really should be achieving of like a flag glucose. So

501
00:26:54,280 --> 00:26:56,520
Speaker 2: I think that's the that's the kind of idea of

502
00:26:56,600 --> 00:26:59,879
Speaker 2: the of the app, but there's not, and there is

503
00:27:00,080 --> 00:27:02,760
Speaker 2: some summary measure like they'll be like, your glucose is fine,

504
00:27:02,760 --> 00:27:04,200
Speaker 2: you know yourte Yeah, I.

505
00:27:04,160 --> 00:27:06,560
Speaker 1: Want I want a green box or something like that.

506
00:27:06,640 --> 00:27:10,160
Speaker 1: I can get it. There are of course companies now

507
00:27:10,359 --> 00:27:14,159
Speaker 1: actually including a company founded by Casey Means, who we

508
00:27:14,200 --> 00:27:17,320
Speaker 1: played at the beginning of this episode, which will not

509
00:27:17,440 --> 00:27:21,080
Speaker 1: only sell you, you know, it's a repackaged continuous glucose monitor

510
00:27:21,119 --> 00:27:24,520
Speaker 1: from one of these companies that you already mentioned, but

511
00:27:24,760 --> 00:27:28,080
Speaker 1: coupled with their own app that is supposed to contest

512
00:27:28,560 --> 00:27:31,280
Speaker 1: use AI and combine and give you give you all

513
00:27:31,320 --> 00:27:34,320
Speaker 1: sorts of health advice, you know. And this is part

514
00:27:34,359 --> 00:27:38,600
Speaker 1: of the problem with kind of access to too much data,

515
00:27:38,760 --> 00:27:41,600
Speaker 1: like like, we have so many ways to monitor ourselves,

516
00:27:41,720 --> 00:27:43,679
Speaker 1: right it started just with step counts, but now we

517
00:27:43,720 --> 00:27:46,240
Speaker 1: have our heart rape variability and our sleep quality, and

518
00:27:46,359 --> 00:27:50,000
Speaker 1: we could potentially do continuous glucose monitoring. That's really outpaced

519
00:27:50,040 --> 00:27:54,120
Speaker 1: our knowledge of what to do with that information, which

520
00:27:54,160 --> 00:27:56,240
Speaker 1: is why these people who are selling you like, oh,

521
00:27:56,240 --> 00:27:58,280
Speaker 1: we're going to use this to give you the answer

522
00:27:58,320 --> 00:28:01,560
Speaker 1: about your health are fundamentally lying because that data just

523
00:28:01,640 --> 00:28:05,480
Speaker 1: doesn't exist yet. Like, you can make recommendations about eating

524
00:28:05,480 --> 00:28:09,920
Speaker 1: healthier and that's fine. And I suppose if seeing big

525
00:28:09,960 --> 00:28:14,440
Speaker 1: deviations in your glucose convinces you to, you know, eat

526
00:28:14,480 --> 00:28:17,600
Speaker 1: some more protein instead of eating that chocolate chip cookie,

527
00:28:17,640 --> 00:28:20,440
Speaker 1: then that's like good biofeedback. But we don't really have outcomes.

528
00:28:20,640 --> 00:28:22,439
Speaker 3: Yeah, so I think this is the real question for

529
00:28:22,480 --> 00:28:24,360
Speaker 3: the data. I think it's kind of two questions.

530
00:28:24,359 --> 00:28:27,600
Speaker 2: So one is when we look at, you know, trials

531
00:28:27,640 --> 00:28:30,560
Speaker 2: of people in the general population use them these, which

532
00:28:30,600 --> 00:28:33,919
Speaker 2: is actually not a huge space, but we don't really

533
00:28:33,960 --> 00:28:39,160
Speaker 2: see much evidence that the use of these improves outcomes

534
00:28:39,360 --> 00:28:39,800
Speaker 2: very much.

535
00:28:39,840 --> 00:28:41,880
Speaker 3: Would you say that's your read of the of the

536
00:28:41,960 --> 00:28:42,920
Speaker 3: data in.

537
00:28:42,960 --> 00:28:47,600
Speaker 1: People without diabetes? Yes, that is that is my read.

538
00:28:47,880 --> 00:28:50,680
Speaker 1: In terms of hard health metrics, there's not much.

539
00:28:50,920 --> 00:28:53,760
Speaker 2: But I think there's a second question, which is is

540
00:28:53,840 --> 00:28:57,320
Speaker 2: this predictive? So one, there's a like a policy question,

541
00:28:57,360 --> 00:28:59,239
Speaker 2: which is if you gave everybody a CGM, would they

542
00:28:59,280 --> 00:29:01,280
Speaker 2: be like less like to have diabetes? And I think

543
00:29:01,280 --> 00:29:03,800
Speaker 2: the answer is like, probably no, at least from what

544
00:29:03,840 --> 00:29:04,520
Speaker 2: we know so far.

545
00:29:04,880 --> 00:29:05,800
Speaker 3: There's a second.

546
00:29:05,520 --> 00:29:09,000
Speaker 2: Question, which is, as a person who's you know, concerned

547
00:29:09,000 --> 00:29:13,000
Speaker 2: about your health. Could you learn something from this that

548
00:29:13,080 --> 00:29:15,560
Speaker 2: would be predictive, that would say, you know, well, you

549
00:29:15,640 --> 00:29:17,760
Speaker 2: actually are at a higher risk for diabetes, you know,

550
00:29:17,800 --> 00:29:21,560
Speaker 2: relative to your demographics, because something is going on with

551
00:29:21,680 --> 00:29:26,160
Speaker 2: your blood sugar. And I think that's like in some

552
00:29:26,200 --> 00:29:28,880
Speaker 2: ways the data there is like a little bit more.

553
00:29:30,160 --> 00:29:30,960
Speaker 3: Complicated.

554
00:29:31,040 --> 00:29:34,000
Speaker 2: So you know, they've done a there's a study from

555
00:29:34,000 --> 00:29:38,320
Speaker 2: the Journal of Diabetes Research which looks at non diabetic

556
00:29:38,320 --> 00:29:42,120
Speaker 2: people using these cgms, and some of them develop diabetes.

557
00:29:42,200 --> 00:29:45,320
Speaker 2: There's of course strong predictors here of age and BMI,

558
00:29:45,480 --> 00:29:49,240
Speaker 2: which we know predict development of diabetes, but there's also

559
00:29:49,360 --> 00:29:52,640
Speaker 2: some evidence that people who spend more time in a

560
00:29:52,800 --> 00:29:56,960
Speaker 2: high blood sugar range above one hundred and thirty, there's

561
00:29:57,000 --> 00:29:58,320
Speaker 2: some predictive capacity.

562
00:29:58,360 --> 00:29:59,920
Speaker 3: It's not very good, but.

563
00:29:59,840 --> 00:30:02,800
Speaker 2: It it does sort of suggest that maybe these could

564
00:30:02,800 --> 00:30:06,120
Speaker 2: pick up something about insulin resistance that would be a

565
00:30:06,120 --> 00:30:07,800
Speaker 2: little bit predictive. I think is like the best that's

566
00:30:07,800 --> 00:30:08,920
Speaker 2: like the best case argument.

567
00:30:09,160 --> 00:30:14,000
Speaker 1: Yeah. So, but this is a classic correlation versus causality

568
00:30:14,320 --> 00:30:17,840
Speaker 1: issue that we often face in this podcast. So there

569
00:30:18,200 --> 00:30:21,160
Speaker 1: are multiple studies which show that among people who do

570
00:30:21,200 --> 00:30:25,280
Speaker 1: not have diabetes, response to an oral glucose tolerance test,

571
00:30:25,360 --> 00:30:27,840
Speaker 1: which is literally like, you come in fasting, we measure

572
00:30:27,880 --> 00:30:31,720
Speaker 1: your blood sugar, we give you really sweet drink to drink,

573
00:30:31,800 --> 00:30:34,000
Speaker 1: and we measure how high your glucose goes up. Like

574
00:30:34,160 --> 00:30:37,880
Speaker 1: the people who go up higher in that test are

575
00:30:37,880 --> 00:30:40,920
Speaker 1: more likely to go on to have diabetes. It could

576
00:30:40,960 --> 00:30:46,000
Speaker 1: be that spending time at high glucose values, you know,

577
00:30:46,400 --> 00:30:50,960
Speaker 1: flogs you're pancreas enough such that you start becoming insulin resistant.

578
00:30:51,800 --> 00:30:55,320
Speaker 1: It also could be that what we're doing with all

579
00:30:55,360 --> 00:30:59,320
Speaker 1: this these testing is just identifying the type of person

580
00:30:59,560 --> 00:31:04,120
Speaker 1: who all already has some insulin problems and the test

581
00:31:04,160 --> 00:31:07,400
Speaker 1: is revealing that, or the continuous glucose monitoring is revealing

582
00:31:07,440 --> 00:31:12,480
Speaker 1: that you have some subclinical insulin resistance, and that's it.

583
00:31:12,640 --> 00:31:17,880
Speaker 1: And the reason that this matters is because if those

584
00:31:17,920 --> 00:31:21,560
Speaker 1: spikes of glucose right that you're seeing on your glucose monitor,

585
00:31:22,640 --> 00:31:27,440
Speaker 1: if those are causing downstream, bad downstream effects like diabetes,

586
00:31:27,760 --> 00:31:32,320
Speaker 1: then limiting those spikes will limit the downstream effects of diabetes.

587
00:31:32,360 --> 00:31:34,360
Speaker 1: And we should be telling people like, oh, yeah, you

588
00:31:34,360 --> 00:31:36,840
Speaker 1: don't want those spikes. The spikes are bad. However, if

589
00:31:36,880 --> 00:31:39,960
Speaker 1: the spikes are just a sign that you are at risk.

590
00:31:40,680 --> 00:31:44,080
Speaker 1: You don't know that limiting it changes that risk profile

591
00:31:44,400 --> 00:31:44,760
Speaker 1: at all.

592
00:31:45,240 --> 00:31:46,960
Speaker 2: And yeah, and then you could say, okay, well, is

593
00:31:46,960 --> 00:31:49,040
Speaker 2: there some other action that people should be taking if

594
00:31:49,040 --> 00:31:51,080
Speaker 2: you knew you were at higher risk for this, would

595
00:31:51,080 --> 00:31:54,320
Speaker 2: you want to be more cautious about various kinds of

596
00:31:54,360 --> 00:31:57,840
Speaker 2: health choices that you make, or more monitoring or so on. Now,

597
00:31:57,920 --> 00:31:59,800
Speaker 2: of course, most of the people who are doing these

598
00:31:59,800 --> 00:32:02,480
Speaker 2: things as a recreational activity are already doing all the

599
00:32:02,520 --> 00:32:05,640
Speaker 2: other things which are going to prevent diabetes. So I

600
00:32:05,680 --> 00:32:07,720
Speaker 2: think it's a little bit it's a little bit tricky there.

601
00:32:07,720 --> 00:32:10,360
Speaker 1: But the way to tease this out, of course, is

602
00:32:10,440 --> 00:32:14,600
Speaker 1: to do randomized trials. And one tip for people who

603
00:32:14,640 --> 00:32:17,600
Speaker 1: are looking at influencers in this space and here's some

604
00:32:17,760 --> 00:32:20,200
Speaker 1: any remarkable claim is always just to you know, quickly

605
00:32:20,240 --> 00:32:24,240
Speaker 1: google the thing they're talking about, randomized trial and the

606
00:32:24,280 --> 00:32:26,560
Speaker 1: claim that they are making, and you'll often get a

607
00:32:26,560 --> 00:32:28,600
Speaker 1: better sense of the truth than whatever people are saying.

608
00:32:29,080 --> 00:32:33,240
Speaker 1: I wanted to bring up a meta analysis, which is

609
00:32:33,280 --> 00:32:37,640
Speaker 1: a summary of trials that randomized people without diabetes to

610
00:32:38,000 --> 00:32:43,160
Speaker 1: continuous glucose monitoring versus no continuous glucose monitoring, and this

611
00:32:43,200 --> 00:32:47,560
Speaker 1: actually showed that the people who used the cgms did

612
00:32:47,600 --> 00:32:52,480
Speaker 1: have a lower mean blood glucose than the people who

613
00:32:52,480 --> 00:32:55,240
Speaker 1: weren't assigned to use cgms. This is a bit of

614
00:32:55,240 --> 00:32:57,760
Speaker 1: a self fulfilling prophecy. It's sort of like if you

615
00:32:57,800 --> 00:33:00,800
Speaker 1: give someone a step counter, they end up taking more

616
00:33:00,800 --> 00:33:04,240
Speaker 1: steps like you just yes, you do like a hawthorn effect.

617
00:33:04,280 --> 00:33:08,520
Speaker 2: Basically like watching you, you're watching you. You want to

618
00:33:08,760 --> 00:33:11,120
Speaker 2: you know, at least in the short term, you're incentivized

619
00:33:11,160 --> 00:33:14,080
Speaker 2: to like achieve whatever is the thing you're looking.

620
00:33:13,880 --> 00:33:17,320
Speaker 1: For exactly, and so that's good at least it's like, Okay,

621
00:33:17,360 --> 00:33:21,640
Speaker 1: there's some conceptual basis here, but but there's no difference

622
00:33:21,720 --> 00:33:24,280
Speaker 1: in their body mass index at the end of the trial.

623
00:33:24,720 --> 00:33:27,040
Speaker 1: And so you know, this is an example of the

624
00:33:27,120 --> 00:33:29,360
Speaker 1: how okay, you got your blood sugar your mean blood

625
00:33:29,360 --> 00:33:31,440
Speaker 1: sugar down a little bit because you're watching it like

626
00:33:31,480 --> 00:33:33,160
Speaker 1: a hawk and you are kind of like, oh my god, no,

627
00:33:33,160 --> 00:33:34,959
Speaker 1: I'm not going to eat that cookie because it's going

628
00:33:35,040 --> 00:33:38,160
Speaker 1: to give me a spike. But in terms of something

629
00:33:38,160 --> 00:33:40,680
Speaker 1: that maybe matters more in terms of outcomes, like your BMI,

630
00:33:41,880 --> 00:33:42,400
Speaker 1: no effect.

631
00:33:43,400 --> 00:33:47,560
Speaker 2: Yeah, I guess my I have a very simple like

632
00:33:47,760 --> 00:33:49,600
Speaker 2: takeaway if somebody came to me they said, I'm thinking

633
00:33:49,640 --> 00:33:50,400
Speaker 2: about getting one.

634
00:33:50,320 --> 00:33:53,160
Speaker 3: Of these, like what like should I?

635
00:33:53,160 --> 00:33:56,160
Speaker 2: I think the question is what is the information For

636
00:33:56,360 --> 00:33:58,000
Speaker 2: the same question I would ask if they told me, like,

637
00:33:58,000 --> 00:34:00,200
Speaker 2: I'm going to get a very complicated blood panel with

638
00:34:00,240 --> 00:34:02,560
Speaker 2: like one hundred things in it. Okay, well, what will

639
00:34:02,600 --> 00:34:05,400
Speaker 2: you be doing? What what action will you be taking

640
00:34:05,440 --> 00:34:08,239
Speaker 2: differently based on this information? And then you know, what

641
00:34:08,360 --> 00:34:10,560
Speaker 2: are you like, what's the information you're looking for? Is

642
00:34:10,600 --> 00:34:12,319
Speaker 2: this going to deliver that? And I think we can

643
00:34:12,320 --> 00:34:14,719
Speaker 2: talk about whether there are situations in which I think

644
00:34:14,719 --> 00:34:19,000
Speaker 2: there are some in which this information could be valuable potentially,

645
00:34:19,320 --> 00:34:22,520
Speaker 2: But that is the question as opposed to just I'm interested.

646
00:34:22,560 --> 00:34:25,399
Speaker 2: So I can like see where this number is, which

647
00:34:25,440 --> 00:34:27,880
Speaker 2: is like, okay, yes, when you eat things, it's going

648
00:34:27,920 --> 00:34:29,920
Speaker 2: to go up. When you're hungry and you're like in

649
00:34:29,920 --> 00:34:31,279
Speaker 2: the middle of the night, it's going to go down.

650
00:34:31,320 --> 00:34:32,440
Speaker 2: I promise you will learn that.

651
00:34:32,520 --> 00:34:36,120
Speaker 1: But like then what, yeah, so what did you learn? Like,

652
00:34:36,360 --> 00:34:38,400
Speaker 1: give me your personal story. You wore this thing for

653
00:34:38,440 --> 00:34:40,799
Speaker 1: two weeks, how did it change your behavior? Why did

654
00:34:40,880 --> 00:34:42,040
Speaker 1: you want in the first place?

655
00:34:42,400 --> 00:34:45,960
Speaker 2: Okay, so I actually had a reason for this. So

656
00:34:46,040 --> 00:34:48,640
Speaker 2: one reason is I'm a person who enjoys self tracking,

657
00:34:48,680 --> 00:34:52,160
Speaker 2: and I was curious. But the main reason was that

658
00:34:52,200 --> 00:34:54,800
Speaker 2: I had had as part of like a standard blood test,

659
00:34:54,880 --> 00:34:57,239
Speaker 2: I had had a glucose test, and my glucose, even

660
00:34:57,280 --> 00:35:00,640
Speaker 2: not fasted, was quite quite low. And so then there

661
00:35:00,719 --> 00:35:02,799
Speaker 2: was a question of like should I, like, is it

662
00:35:02,840 --> 00:35:06,040
Speaker 2: so low that I should be worried about it? And

663
00:35:06,200 --> 00:35:11,000
Speaker 2: so I wore this thing and and I learned like

664
00:35:11,360 --> 00:35:17,000
Speaker 2: two things. One is that my glucose is sometimes like

665
00:35:17,840 --> 00:35:19,960
Speaker 2: below the bottom of the range is fifty five, and

666
00:35:20,000 --> 00:35:23,640
Speaker 2: like I'm frequently dropping like under the like it like

667
00:35:23,719 --> 00:35:26,920
Speaker 2: grays out at the bottom of the range, and so

668
00:35:27,120 --> 00:35:29,800
Speaker 2: I like sometimes have very low glugos. But I also

669
00:35:29,960 --> 00:35:32,120
Speaker 2: then learned I have no symptoms of that. And so

670
00:35:32,600 --> 00:35:34,880
Speaker 2: my doctor was like, I don't know something. It's the

671
00:35:34,960 --> 00:35:36,960
Speaker 2: problem That was an example of something was like why

672
00:35:37,000 --> 00:35:40,560
Speaker 2: did I know that? It's really irrelevant that I did

673
00:35:40,640 --> 00:35:45,239
Speaker 2: learn one actionable thing, which was about how I respond

674
00:35:45,320 --> 00:35:48,640
Speaker 2: to eating carbohydrates while I run, which is that I

675
00:35:48,680 --> 00:35:51,359
Speaker 2: respond quite a lot in a positive way, and so

676
00:35:51,680 --> 00:35:54,520
Speaker 2: I like you can sort of see when I'm I

677
00:35:54,520 --> 00:35:56,480
Speaker 2: could see when I was running, like when I took

678
00:35:56,520 --> 00:35:59,400
Speaker 2: a gel these running gels have like a very like

679
00:35:59,440 --> 00:36:01,840
Speaker 2: a very very concentrated amount of like sugar.

680
00:36:02,320 --> 00:36:05,520
Speaker 3: It's like you know, like forty grams of carbohydrates.

681
00:36:04,840 --> 00:36:08,000
Speaker 2: Like in the like yeah, and so I started taking

682
00:36:08,040 --> 00:36:11,160
Speaker 2: those earlier in my run. I moved my gel fueling

683
00:36:11,239 --> 00:36:14,279
Speaker 2: timing from four eight twelve sixteen to two six ten

684
00:36:14,520 --> 00:36:15,240
Speaker 2: on the mileage.

685
00:36:15,680 --> 00:36:17,360
Speaker 3: And that's that's what I learned.

686
00:36:17,640 --> 00:36:21,640
Speaker 1: You've lost me at oh that's the number of miles. Jesus, Emily,

687
00:36:21,680 --> 00:36:25,600
Speaker 1: Oh my god. You're such an athlete. So when you

688
00:36:25,600 --> 00:36:28,000
Speaker 1: say like you timed it better, you're watching it and

689
00:36:28,040 --> 00:36:30,279
Speaker 1: you're like you're running and your glucose is kind of

690
00:36:30,280 --> 00:36:32,000
Speaker 1: coming down or something as you run, and.

691
00:36:31,920 --> 00:36:33,960
Speaker 2: Then like, yeah, I didn't watch it while I was

692
00:36:34,040 --> 00:36:36,279
Speaker 2: running because you would bump your head.

693
00:36:36,520 --> 00:36:37,880
Speaker 3: Afterwards, I could see.

694
00:36:37,760 --> 00:36:41,640
Speaker 2: Like basically that I was like like there was a

695
00:36:41,680 --> 00:36:44,320
Speaker 2: reason that at three miles I was often feeling tired,

696
00:36:44,360 --> 00:36:46,400
Speaker 2: which is like I could see my glucose was like

697
00:36:46,440 --> 00:36:50,640
Speaker 2: at fifty or something has rich. And then even if

698
00:36:50,680 --> 00:36:52,600
Speaker 2: I did nothing, this is part of like the body

699
00:36:52,680 --> 00:36:54,279
Speaker 2: says me, even if I eat nothing, it will come

700
00:36:54,320 --> 00:36:57,239
Speaker 2: back up. But if I took a gel at two

701
00:36:57,320 --> 00:37:00,320
Speaker 2: miles rather than waiting to four miles, and then I

702
00:37:00,360 --> 00:37:04,120
Speaker 2: could basically stave off that low point because the glucose

703
00:37:04,120 --> 00:37:06,640
Speaker 2: would maintain at a higher range.

704
00:37:06,880 --> 00:37:09,840
Speaker 1: You're no doubt aware of this. But the fun physiology here,

705
00:37:10,200 --> 00:37:13,359
Speaker 1: this hitting the wall physiology, is when you deplete all

706
00:37:13,400 --> 00:37:17,120
Speaker 1: your liver glycogen stores. So you know, as I said

707
00:37:17,120 --> 00:37:19,680
Speaker 1: at the beginning, there's actually very little glucose floating around

708
00:37:19,680 --> 00:37:22,040
Speaker 1: your blood at any given time, like a teaspoons worth,

709
00:37:22,120 --> 00:37:25,160
Speaker 1: so obviously not enough to sustain a long run, but

710
00:37:25,360 --> 00:37:28,640
Speaker 1: your liver converts glucose into glycogen, just sticks a bunch

711
00:37:28,680 --> 00:37:32,719
Speaker 1: of glucoses together basically, which sort of it's like the

712
00:37:32,920 --> 00:37:35,240
Speaker 1: running gel of the body, like it can be quickly

713
00:37:35,280 --> 00:37:37,880
Speaker 1: delivered back into the bloodstream and broken down into glucose,

714
00:37:38,680 --> 00:37:41,640
Speaker 1: but there's a limited amount of it. And typically, yeah,

715
00:37:41,719 --> 00:37:44,000
Speaker 1: after a depending on what kind of athlete you are

716
00:37:44,640 --> 00:37:46,360
Speaker 1: and how much you've eaten and all that kind of

717
00:37:46,360 --> 00:37:49,200
Speaker 1: stuff and carbo loading and things like that, you'll deplete

718
00:37:49,280 --> 00:37:51,440
Speaker 1: your liver glycogen and you know, let's say two or

719
00:37:51,440 --> 00:37:55,759
Speaker 1: three miles, and then you switch to what's called glucooneogenesis,

720
00:37:55,840 --> 00:37:59,520
Speaker 1: which is creating glucose from other stuff, right breaking down

721
00:37:59,560 --> 00:38:02,200
Speaker 1: protein breaking down fats, so on and so forth, and

722
00:38:02,280 --> 00:38:05,600
Speaker 1: you do feel that difference. Like you know, athletes like

723
00:38:05,640 --> 00:38:08,120
Speaker 1: yourself probably know that feeling of like I'm running, everything

724
00:38:08,120 --> 00:38:10,239
Speaker 1: feels fine, all of a sudden, I'm like dragging a

725
00:38:10,239 --> 00:38:13,799
Speaker 1: little bit. And you know, if you're very in tune

726
00:38:13,800 --> 00:38:15,719
Speaker 1: with your body, maybe you know that you feel that

727
00:38:15,800 --> 00:38:18,040
Speaker 1: coming and you take your glucose gel. But maybe if

728
00:38:18,080 --> 00:38:20,480
Speaker 1: you're not, a CGM would be useful here.

729
00:38:20,800 --> 00:38:23,360
Speaker 2: And this is actually, I will say, in the endurance

730
00:38:23,400 --> 00:38:28,200
Speaker 2: sports space, people use these for this exact reason, which

731
00:38:28,239 --> 00:38:31,320
Speaker 2: is to sort of figure out what is the timing

732
00:38:31,600 --> 00:38:35,560
Speaker 2: and approach to fueling that allows them to maintain a

733
00:38:35,640 --> 00:38:40,760
Speaker 2: high level of glucose throughout a long distance endurance sports activity. Okay,

734
00:38:40,840 --> 00:38:44,359
Speaker 2: because that is like the easiest way to like you

735
00:38:44,400 --> 00:38:47,080
Speaker 2: basically don't want to get into that well because then

736
00:38:47,120 --> 00:38:49,000
Speaker 2: you can't move your muscles as fast.

737
00:38:49,160 --> 00:38:52,759
Speaker 3: So the cycling peloton, like.

738
00:38:52,680 --> 00:38:55,440
Speaker 2: The guys who ride the Tour de Frants, they were

739
00:38:55,560 --> 00:38:58,799
Speaker 2: using these to like try to optimize their fueling and

740
00:38:58,840 --> 00:39:01,360
Speaker 2: then they got bannedlways feel like when something is banned

741
00:39:01,360 --> 00:39:05,160
Speaker 2: in the night peleton, you know that works because these

742
00:39:05,160 --> 00:39:07,120
Speaker 2: guys are like they're looking for the one percent.

743
00:39:07,239 --> 00:39:10,719
Speaker 1: You know, I buy that metric, But just to say

744
00:39:10,719 --> 00:39:14,279
Speaker 1: it out loud, do we have any harder evidence than

745
00:39:14,640 --> 00:39:18,160
Speaker 1: doping bands for endurance athletes in the use of cgms.

746
00:39:18,280 --> 00:39:21,440
Speaker 3: Is there any harder evidence than doping bands for anything.

747
00:39:22,239 --> 00:39:26,000
Speaker 2: Yes, we have a little bit more harder evidence in sports.

748
00:39:26,000 --> 00:39:29,640
Speaker 2: So they've done some stuff in cyclists and runners who

749
00:39:29,640 --> 00:39:34,640
Speaker 2: are doing longer efforts, and they basically find that if

750
00:39:34,680 --> 00:39:40,440
Speaker 2: you give people a CGM it is helpful at optimizing

751
00:39:40,480 --> 00:39:43,600
Speaker 2: their carbohydrate intake so they don't drop.

752
00:39:43,360 --> 00:39:45,399
Speaker 3: Below seventy milligrams.

753
00:39:45,680 --> 00:39:49,320
Speaker 2: So it's not like there's just like a direct feedback

754
00:39:49,360 --> 00:39:53,200
Speaker 2: exactly the thing I describe, which is for obvious reasons,

755
00:39:53,520 --> 00:39:55,719
Speaker 2: if you use this to then inform your fueling, then

756
00:39:55,760 --> 00:39:57,360
Speaker 2: you can do better at your fueling.

757
00:39:57,600 --> 00:39:59,920
Speaker 1: I think it's interesting though, that like the best use

758
00:40:00,120 --> 00:40:02,680
Speaker 1: case maybe we've come up with so far for people

759
00:40:02,719 --> 00:40:06,360
Speaker 1: without diabetes or pre diabetes is not keeping your glucose

760
00:40:06,600 --> 00:40:07,880
Speaker 1: from getting too high.

761
00:40:08,000 --> 00:40:09,920
Speaker 3: It's actually from getting to lose.

762
00:40:09,960 --> 00:40:12,399
Speaker 1: It's like watching out from getting too low while you're

763
00:40:12,480 --> 00:40:14,960
Speaker 1: while you're consuming a lot of glucose in your muscles.

764
00:40:15,320 --> 00:40:17,040
Speaker 3: Yes, that is maybe what we learn.

765
00:40:18,160 --> 00:40:20,239
Speaker 2: Okay, are there any I think the last thing I

766
00:40:20,280 --> 00:40:23,000
Speaker 2: want to talk about here, so like to sort of

767
00:40:23,040 --> 00:40:25,880
Speaker 2: put a pin in that. I think the question people

768
00:40:25,880 --> 00:40:27,600
Speaker 2: can ask themselves is you know, what am I planning

769
00:40:27,640 --> 00:40:29,000
Speaker 2: to What am I planning to do with this?

770
00:40:29,080 --> 00:40:31,080
Speaker 3: And not everybody's going to have the like I'm running

771
00:40:31,120 --> 00:40:32,600
Speaker 3: for two hours.

772
00:40:32,560 --> 00:40:35,760
Speaker 2: Version of this, But I think there are cases for saying,

773
00:40:35,800 --> 00:40:39,880
Speaker 2: you know, I'm feeling really tired or really draggy in

774
00:40:39,920 --> 00:40:42,520
Speaker 2: different times of the day, like maybe there's some information

775
00:40:42,640 --> 00:40:44,719
Speaker 2: in here that could be that could you know, in

776
00:40:44,719 --> 00:40:46,160
Speaker 2: some way be helpful.

777
00:40:46,520 --> 00:40:49,640
Speaker 1: Yeah, it feels like though your your point about like

778
00:40:49,800 --> 00:40:54,040
Speaker 1: a two week fifty dollars investment, maybe okay, just to

779
00:40:54,160 --> 00:40:56,920
Speaker 1: like play around and get a sense of, you know,

780
00:40:57,080 --> 00:41:00,880
Speaker 1: your body, I'm not feeling like, oh yes, and therefore

781
00:41:00,920 --> 00:41:02,840
Speaker 1: you need the monthly subscription, And like.

782
00:41:02,960 --> 00:41:04,400
Speaker 3: No, I think I would never.

783
00:41:04,560 --> 00:41:08,480
Speaker 2: I think there's almost no case for someone to do

784
00:41:08,600 --> 00:41:11,879
Speaker 2: anything other than get fifty spend fifty bucks for two weeks,

785
00:41:11,920 --> 00:41:13,440
Speaker 2: and I think for most people there's no case for

786
00:41:13,480 --> 00:41:16,320
Speaker 2: that either. But the idea that you're going to endlessly

787
00:41:16,360 --> 00:41:18,719
Speaker 2: wear one of these, you know, I will say I

788
00:41:18,719 --> 00:41:20,439
Speaker 2: wore this like three days in I was like, okay,

789
00:41:20,440 --> 00:41:24,080
Speaker 2: I've learned something and then it, you know, a week

790
00:41:24,120 --> 00:41:26,000
Speaker 2: in or ten days in, it fell off when I

791
00:41:26,040 --> 00:41:28,680
Speaker 2: hit it with the laundry machine door.

792
00:41:28,840 --> 00:41:30,680
Speaker 3: And so then I just was like, now that's probably

793
00:41:30,760 --> 00:41:31,360
Speaker 3: enough information.

794
00:41:32,520 --> 00:41:36,759
Speaker 1: We should talk about some potential risks, yes, because nothing

795
00:41:36,840 --> 00:41:40,080
Speaker 1: is risk for free. Obviously, it's breaking the skin, you know,

796
00:41:40,160 --> 00:41:42,360
Speaker 1: some theoretical risk of infection, and of course there's the

797
00:41:42,400 --> 00:41:44,759
Speaker 1: risk of spending money, which you have other things to

798
00:41:44,840 --> 00:41:48,719
Speaker 1: spend your hard earned money on opportunity cost. But there

799
00:41:48,880 --> 00:41:52,919
Speaker 1: is a new psychological condition. It has not yet made

800
00:41:52,920 --> 00:41:55,880
Speaker 1: it all the way to the DSM, the diagnostics and

801
00:41:55,920 --> 00:41:59,120
Speaker 1: statistical manual which are all the official psychiatric conditions, but

802
00:41:59,160 --> 00:42:02,200
Speaker 1: it's called ortho rexia nervosa. Have you heard of this.

803
00:42:02,880 --> 00:42:07,440
Speaker 2: Yeah, it's like an over obsession with health. Yeah, eating

804
00:42:07,560 --> 00:42:09,480
Speaker 2: healthy behaviors.

805
00:42:09,440 --> 00:42:10,280
Speaker 1: Yeah, exactly.

806
00:42:10,360 --> 00:42:10,440
Speaker 2: So.

807
00:42:11,080 --> 00:42:15,320
Speaker 1: Orthorexia nervosa has been documented in several studies. Again, not

808
00:42:15,360 --> 00:42:18,880
Speaker 1: an official psychological diagnosis yet, but it's basically people who

809
00:42:18,960 --> 00:42:21,920
Speaker 1: become so obsessed and continuous glucose monitors can play into

810
00:42:22,000 --> 00:42:26,239
Speaker 1: this with like maintaining that perfectly flat line that it

811
00:42:26,280 --> 00:42:30,319
Speaker 1: starts to consume them and every psychological condition that does

812
00:42:30,440 --> 00:42:33,000
Speaker 1: end up in the DSM always has a criteria that's

813
00:42:33,080 --> 00:42:35,680
Speaker 1: like and it interferes with your daily life, right, Like,

814
00:42:35,960 --> 00:42:39,240
Speaker 1: so you know alcohol use disorder. It's like you're drinking

815
00:42:39,280 --> 00:42:42,239
Speaker 1: alcohol and it interferes with your daily life. And so

816
00:42:42,920 --> 00:42:46,560
Speaker 1: there are people for whom things like this. The cgms,

817
00:42:46,760 --> 00:42:51,080
Speaker 1: even sleep trackers that's called orthosomnia, like gets so obsessed

818
00:42:51,400 --> 00:42:55,800
Speaker 1: with kind of perfecting those metrics that their relationships suffer,

819
00:42:55,960 --> 00:42:58,359
Speaker 1: their job suffer. It's all they can think about. If

820
00:42:58,400 --> 00:43:00,640
Speaker 1: you feel like you're that type of person, do not

821
00:43:00,680 --> 00:43:02,560
Speaker 1: put one of these on your body.

822
00:43:02,960 --> 00:43:05,560
Speaker 2: I agree, And I think the other piece of this

823
00:43:05,840 --> 00:43:09,680
Speaker 2: is I do not think people should fear foods.

824
00:43:10,360 --> 00:43:11,800
Speaker 3: And there's a.

825
00:43:11,400 --> 00:43:13,719
Speaker 2: Little bit of a sort of space in here, and

826
00:43:13,760 --> 00:43:15,840
Speaker 2: you see this in the wellness influencer space. It's like,

827
00:43:15,840 --> 00:43:18,160
Speaker 2: you know, I wore a CGM, and I learned that

828
00:43:18,239 --> 00:43:22,040
Speaker 2: I can never eat rice because when you eat rice,

829
00:43:22,200 --> 00:43:25,759
Speaker 2: your glucose spikes. In this way, you can only eat

830
00:43:25,800 --> 00:43:28,160
Speaker 2: rice if you also put chicken on it. It's like,

831
00:43:28,440 --> 00:43:31,040
Speaker 2: it's fine to eat chicken with your rice. But I

832
00:43:31,080 --> 00:43:33,920
Speaker 2: don't think that. I worry that people will sort of

833
00:43:34,200 --> 00:43:39,760
Speaker 2: overinterpret just like basic variations in glucose that are totally normal,

834
00:43:39,840 --> 00:43:43,359
Speaker 2: and physiology and the basic physiology as some kind of

835
00:43:43,440 --> 00:43:46,399
Speaker 2: like I'm broken because you know, my blood sugar went

836
00:43:46,400 --> 00:43:48,680
Speaker 2: to one hundred and thirty after I ate a bowl

837
00:43:48,680 --> 00:43:52,840
Speaker 2: of rice, which is a totally normal, regular thing to

838
00:43:52,920 --> 00:43:53,640
Speaker 2: have happened.

839
00:43:53,920 --> 00:43:58,480
Speaker 1: Yeah, And just to say, even larger spikes, well documented

840
00:43:59,200 --> 00:44:02,520
Speaker 1: spikes up to one sixty one, seventy one to ninety

841
00:44:02,640 --> 00:44:05,080
Speaker 1: after a meal, even up to two hundred in healthy

842
00:44:05,080 --> 00:44:08,880
Speaker 1: individuals can be normal depending on the amount of glucose

843
00:44:08,920 --> 00:44:11,759
Speaker 1: that you ingest. And it doesn't mean that you're necessarily

844
00:44:11,800 --> 00:44:14,919
Speaker 1: insulin resistant or broken in any way. It just means

845
00:44:14,960 --> 00:44:18,200
Speaker 1: your body really efficiently broke down those carbohydrates and gave

846
00:44:18,200 --> 00:44:20,239
Speaker 1: you a bunch of glucose, and then your body's going

847
00:44:20,320 --> 00:44:22,040
Speaker 1: to do the thing that your body does, which is

848
00:44:22,080 --> 00:44:24,040
Speaker 1: take all that excess glucose and stick it in the

849
00:44:24,040 --> 00:44:26,000
Speaker 1: liver in the form of glycogen so that when you

850
00:44:26,000 --> 00:44:29,760
Speaker 1: go on your run, you don't fall down flat exactly.

851
00:44:30,920 --> 00:44:36,280
Speaker 2: Okay, Perry, continuous glucose monitors for non diabetic individuals, smash

852
00:44:36,280 --> 00:44:37,959
Speaker 2: your pass pass.

853
00:44:37,800 --> 00:44:40,160
Speaker 1: On this one. I think save your money. I don't

854
00:44:40,160 --> 00:44:43,959
Speaker 1: think you're getting much information from it. Focus on something else,

855
00:44:44,120 --> 00:44:44,800
Speaker 1: how about Emily.

856
00:44:45,120 --> 00:44:48,080
Speaker 2: I think I'm also a pass even as a consumer.

857
00:44:48,880 --> 00:44:53,120
Speaker 2: I think for almost no one, is this really very useful?

858
00:44:53,120 --> 00:44:54,680
Speaker 2: And I think a lot of the feedback that you

859
00:44:54,680 --> 00:44:56,839
Speaker 2: would get from it, you could actually just get from

860
00:44:56,960 --> 00:44:59,960
Speaker 2: paying attention to how you feel, which is actually more

861
00:45:00,080 --> 00:45:02,520
Speaker 2: important than what some number is on an app.

862
00:45:03,000 --> 00:45:06,279
Speaker 1: All right, that's it for continuous glucose monitors. Your mail

863
00:45:06,280 --> 00:45:07,960
Speaker 1: bag Question of the Week after the.

864
00:45:07,920 --> 00:45:16,920
Speaker 4: Break, Hey, Emily and Perry. This is Adam from Persipity,

865
00:45:16,960 --> 00:45:19,600
Speaker 4: New Jersey. I had a question for you about statins

866
00:45:19,920 --> 00:45:22,759
Speaker 4: and grapefruit. I've heard that you're not supposed to eat

867
00:45:22,760 --> 00:45:25,560
Speaker 4: grapefruit while you're taking statins, and I didn't know. Is

868
00:45:25,560 --> 00:45:27,440
Speaker 4: it because they won't work as well? Is it that

869
00:45:27,440 --> 00:45:30,600
Speaker 4: they're bad for you if you do? What's the deal?

870
00:45:31,000 --> 00:45:31,600
Speaker 4: Thanks so much?

871
00:45:32,040 --> 00:45:34,200
Speaker 1: Yeah, A lot of people always wonder, you know, it's

872
00:45:34,239 --> 00:45:37,920
Speaker 1: really weird that on certain medications, it's like, you know, oh,

873
00:45:37,960 --> 00:45:41,240
Speaker 1: take this with food, take this without food. Don't eat

874
00:45:41,280 --> 00:45:44,680
Speaker 1: grapefruit while you're having this medication. It feels very specific,

875
00:45:45,040 --> 00:45:47,080
Speaker 1: and it is, and it all has to do with

876
00:45:47,320 --> 00:45:53,719
Speaker 1: liver metabolism. So grapefruit and grapefruit juice contains something called

877
00:45:53,719 --> 00:45:59,520
Speaker 1: a ferano cumerin, which inhibits one of the enzymes in

878
00:45:59,560 --> 00:46:03,120
Speaker 1: your liver. It's responsible for breaking drown some drugs, including

879
00:46:03,200 --> 00:46:06,439
Speaker 1: some status called cytochrome P four fifty and so if

880
00:46:06,480 --> 00:46:09,879
Speaker 1: that enzyme is inhibited, then when you take a dose

881
00:46:09,920 --> 00:46:12,080
Speaker 1: of that drug, it doesn't get broken down as fast

882
00:46:12,160 --> 00:46:14,560
Speaker 1: and it can get to toxic levels more quickly.

883
00:46:15,080 --> 00:46:17,799
Speaker 2: So it's not jost or it's not that it doesn't work,

884
00:46:17,880 --> 00:46:19,719
Speaker 2: it's that it is actively dangerous.

885
00:46:20,120 --> 00:46:24,160
Speaker 1: Correct. Yeah, this is one of those things that makes

886
00:46:24,520 --> 00:46:27,839
Speaker 1: drug levels higher, not lower. But there are other things

887
00:46:27,880 --> 00:46:31,640
Speaker 1: that interact with the liver too that will increase the

888
00:46:31,680 --> 00:46:36,080
Speaker 1: metabolism of certain drugs. There's the most famous one of

889
00:46:36,080 --> 00:46:39,960
Speaker 1: those probably is the interaction between alcohol and thileanol in

890
00:46:40,000 --> 00:46:43,000
Speaker 1: the liver. Those are both metabolized by the same receptor,

891
00:46:43,120 --> 00:46:48,520
Speaker 1: leading to something called the therapeutic misadventure, which is when

892
00:46:49,440 --> 00:46:52,000
Speaker 1: this is kind of fastening and sad, when people try

893
00:46:52,280 --> 00:46:58,240
Speaker 1: to overdose or commit suicide by taking tilenol and alcohol together.

894
00:46:59,200 --> 00:47:03,640
Speaker 1: The alcohols can heating for the thailanol receptor in the liver.

895
00:47:03,760 --> 00:47:05,640
Speaker 1: The thing that breaks down the thaileanol, and it is

896
00:47:05,680 --> 00:47:08,800
Speaker 1: the breakdown product of thailanol that's toxic in the high doses,

897
00:47:08,840 --> 00:47:12,080
Speaker 1: and so by drinking while you did that, you paradoxically

898
00:47:12,160 --> 00:47:14,399
Speaker 1: sort of saved yourself. We see that from time to time.

899
00:47:14,800 --> 00:47:15,240
Speaker 3: Yikes.

900
00:47:15,719 --> 00:47:17,640
Speaker 1: Okay, so don't do any of these things.

901
00:47:17,880 --> 00:47:25,080
Speaker 2: But to get back to the statins.

902
00:47:23,200 --> 00:47:25,640
Speaker 1: Tangential, I'm gonna have my I'm gonna have my.

903
00:47:25,520 --> 00:47:27,080
Speaker 3: Thin min I think you need.

904
00:47:29,440 --> 00:47:31,040
Speaker 2: Does this mean you can literally like, if you go

905
00:47:31,080 --> 00:47:33,239
Speaker 2: on a satin for life, you're never eating grapefruit again.

906
00:47:33,960 --> 00:47:39,520
Speaker 1: That's well, now I'm chewing. Okay, you need to ask

907
00:47:39,520 --> 00:47:40,480
Speaker 1: that question longer.

908
00:47:43,400 --> 00:47:46,799
Speaker 2: I love grape and I never have them again if

909
00:47:46,840 --> 00:47:49,640
Speaker 2: I'm on a statin. Now.

910
00:47:49,719 --> 00:47:54,200
Speaker 1: The truth is, most of the studies of the pharmacokinetics

911
00:47:54,200 --> 00:47:58,520
Speaker 1: of these drugs pharmacokinetics is like the concentration that drugs

912
00:47:58,560 --> 00:48:01,080
Speaker 1: get to in your in your blood after you take them,

913
00:48:01,400 --> 00:48:05,560
Speaker 1: are based on people drinking double concentrated grapefruit juice in

914
00:48:05,920 --> 00:48:10,080
Speaker 1: a significant quantity, and so probably like a bit of

915
00:48:10,080 --> 00:48:12,480
Speaker 1: grapefruit juice here and there is not going to cause

916
00:48:12,520 --> 00:48:15,720
Speaker 1: a huge problem. And of course statins aren't particularly toxic

917
00:48:16,280 --> 00:48:19,319
Speaker 1: at higher doses anyway. So a lot of these risks

918
00:48:19,320 --> 00:48:21,239
Speaker 1: are more theoretical, but they are there.

919
00:48:21,600 --> 00:48:23,640
Speaker 2: But if you were going to plan to be honest

920
00:48:23,640 --> 00:48:26,200
Speaker 2: at and while also being on the grapefruit diet of

921
00:48:26,239 --> 00:48:29,799
Speaker 2: the nineteen seventies, which was exclusively grapefruit oriented, that would

922
00:48:29,840 --> 00:48:30,400
Speaker 2: be a mistake.

923
00:48:30,600 --> 00:48:31,840
Speaker 1: That would clearly be a mistake.

924
00:48:32,040 --> 00:48:37,680
Speaker 3: Okay, news, you can use people. That's it for us today.

925
00:48:37,920 --> 00:48:40,440
Speaker 2: Stick with us next week when we'll ask what's the

926
00:48:40,480 --> 00:48:47,040
Speaker 2: deal with hormone replacement therapy? Wellness Actually is produced in

927
00:48:47,080 --> 00:48:48,720
Speaker 2: association with iHeartMedia.

928
00:48:49,280 --> 00:48:51,200
Speaker 3: Our senior producer is Tamar Avishai.

929
00:48:51,640 --> 00:48:55,279
Speaker 2: Our executive producer at iHeart is Jennifer Bassett. Our theme

930
00:48:55,360 --> 00:48:57,960
Speaker 2: music is by Eric Deutsch, and our content is for

931
00:48:58,080 --> 00:48:59,319
Speaker 2: educational purposes only.

932
00:49:00,040 --> 00:49:02,279
Speaker 1: If you like the show, help other people find us.

933
00:49:02,640 --> 00:49:05,399
Speaker 1: Leave a rating and review on Apple Podcasts or your

934
00:49:05,480 --> 00:49:08,239
Speaker 1: podcatcher of choice, and help us spread the word about

935
00:49:08,239 --> 00:49:11,280
Speaker 1: the show. You can follow us on Instagram at Wellness

936
00:49:11,320 --> 00:49:14,480
Speaker 1: Actually pod and don't forget We want to hear from you.

937
00:49:14,920 --> 00:49:17,440
Speaker 1: Head over to Wellness Actually dot fm and leave us

938
00:49:17,440 --> 00:49:20,040
Speaker 1: a question for our mailbag or suggest a topic for

939
00:49:20,080 --> 00:49:20,759
Speaker 1: a future show.

940
00:49:21,520 --> 00:49:23,399
Speaker 3: We'll let the influencers have the last word.

941
00:49:23,920 --> 00:49:26,560
Speaker 4: I am not a diabetic, but I decided to get

942
00:49:26,640 --> 00:49:29,720
Speaker 4: my CGM because I wanted to find out what foods

943
00:49:29,800 --> 00:49:30,320
Speaker 4: affect me.

944
00:49:30,680 --> 00:49:32,200
Speaker 5: So here are some of the big things that I've

945
00:49:32,280 --> 00:49:33,279
Speaker 5: learned in the past.

946
00:49:33,040 --> 00:49:35,560
Speaker 1: Two weeks of wearing my CTM food.

947
00:49:35,280 --> 00:49:36,399
Speaker 5: Despite my blood sugar.

948
00:49:36,480 --> 00:49:41,800
Speaker 1: The most was drumroll please, sushi.