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Speaker 1: Emily. I don't know if I've ever told you this,

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Speaker 1: but this is not the first podcast idea I've ever had. Well,

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Speaker 1: this actually is the second on the list, and the first, okay,

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Speaker 1: ready was going to be it's me and my wife

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Speaker 1: were both doctors, as you know, and we were going

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Speaker 1: to sit and drink alcohol and talk through medical studies together.

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Speaker 1: And do you know what I was going to call it?

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Speaker 2: No, But first of all, I love it, and I

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Speaker 2: hope you should also. I think you should also make

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Speaker 2: this if one podcast is not enough, I would listen

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Speaker 2: to that. But what is the title?

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Speaker 1: Docs on the Rocks?

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Speaker 2: That is so plascular? I love everything about it. Is

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Speaker 2: the problem that your wife is too smart to get

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Speaker 2: involved with this with you?

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Speaker 1: Yes, that is one hundred percent of problem. I was enthusiastic.

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Speaker 1: I brought as I bought two microphones. I was like,

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Speaker 1: and it'll be like date night. It's like, but we're

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Speaker 1: going to record it and we're gonna have drinks and

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Speaker 1: then there would be a segment at the end, maybe

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Speaker 1: at the beginning of the end. I didn't really think

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Speaker 1: it through, but like, what are we drinking tonight? Like

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Speaker 1: maybe one night it's some wine, maybe something maybe it's

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Speaker 1: a fancy cocktail of some kind, and you know, I

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Speaker 1: would be me and she would be like a normal person,

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Speaker 1: a more normal person. I think it works, and I

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Speaker 1: think it's so.

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Speaker 2: Great, and I'm also feel like she said no, and

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Speaker 2: then you were like, who's next. I'll go to Emily.

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Speaker 2: She'll probably come. You'll probably be Emily's game.

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Speaker 1: But I'm excited that to at least like have a

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Speaker 1: little bit of the vibe of docks on the rocks

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Speaker 1: right now because we're talking about alcohol though it's early.

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Speaker 2: When we're Perry, what are you drinking?

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Speaker 1: Drink any It's just a vodka enema, so technically nothing.

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Speaker 2: I'm drinking a Fair Life Nutrition Plan protein shake, which

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Speaker 2: is a touch of gin.

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Speaker 1: Okay, alcohol coming right up.

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Speaker 2: I'm Emily. 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, July twenty third, twenty twenty six. And this

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Speaker 2: is wellness.

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Speaker 1: Actually, because you're getting a staggering amount of health and

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

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Speaker 1: it is awesome and.

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Speaker 2: Some of it is well actually bull 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, a note that this podcast

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Speaker 1: is for educational purposes and should not be construed as

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Speaker 1: medical advice. We don't know your unique situation, so talk

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Speaker 1: to your doctor for personal health decisions.

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Speaker 2: This week we're asking what's the deal with alcohol? Harry

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

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

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Speaker 2: let's do the health news roundup. After the break.

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Speaker 1: And we're back with the health news of the week. Emily,

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Speaker 1: there is one thing that keeps coming across my health

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Speaker 1: news feed over and over and over again as frequently

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Speaker 1: as many people across the country are running to the bathroom,

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Speaker 1: and that is, of course, cyclospora. What is the latest

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Speaker 1: status of the cyclospora outbreak?

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Speaker 2: So I should say we're recording this at ten thirty

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Speaker 2: six am on Tuesday, so it is very possible that

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Speaker 2: something else will have happened. I cannot explain to you

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Speaker 2: how insane the public health messaging has been around this.

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Speaker 2: So at the end of last week they had identified

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Speaker 2: Iceberg Lettuce as a core cause. And the way they

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Speaker 2: did that is like in Michigan, they talked to a

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Speaker 2: subset of the people who had the explosive diarrhea, like

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Speaker 2: a huge share of them had eaten a Taco Bell

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Speaker 2: and of those, like ninety percent had eaten iceberg lettuce

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Speaker 2: at Taco Bell. So it was like, okay, it's probably

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Speaker 2: the iceberg letus from Taco Bell. And then they traced

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Speaker 2: that back to Taylor Farms, who traced it back to

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Speaker 2: a single plant in Mexico, who then said, okay, let's

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Speaker 2: just recall all of the iceberg lettuce from that plant

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Speaker 2: because it seems plausible it has cyclospora. That felt like, okay,

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Speaker 2: we're kind of getting there. That seems like the answer.

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Speaker 1: This is how epidemiology works.

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Speaker 2: Then over the weekend, the FDA said that they had

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Speaker 2: tested some iceberg lettuce, not from Taco Bell, but some

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Speaker 2: other iceberg lettuce from Tailor Farms, and they had found

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Speaker 2: cyclospora parasites on it. And it was like, okay, that's

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Speaker 2: like check. Now we're even more sure.

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Speaker 1: Yeah.

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Speaker 2: Then they walked that back and they said, actually, that

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Speaker 2: particular test maybe was a false positive. Okay, everything else

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Speaker 2: is still true. All the taco bell stuff, all the epidemiology,

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Speaker 2: all of this is still true. But then Taylor Farms said, oh,

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Speaker 2: now the FDA has said it's not us, which is

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Speaker 2: not what the FDA said. And then the FDA was like, no,

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Speaker 2: we didn't, we didn't apologize, and then people started talking

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Speaker 2: about how Taylor Farms had given Donald Trump a million

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Speaker 2: dollars like over the weekend. And I think fundamentally the

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Speaker 2: messages donate Iceberg. Let us right now. But it could

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Speaker 2: not be more stupid.

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Speaker 1: I confusing, It is so confusing. I did dig in

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Speaker 1: a little bit to like the various tests that are

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Speaker 1: used to essay for cyclospora and the false positive rates.

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Speaker 1: I mean they're not zero, but they're very low. You know,

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Speaker 1: they have specificity of ninety eight to one hundred percent,

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Speaker 1: so false positive rates of sub two percent. So there's

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Speaker 1: I mean, it's always possible.

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Speaker 2: Of course, now you're the tin hat. You're joining the

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Speaker 2: tin Hath conspiracy theory of like the FDA is being

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Speaker 2: bought off by something.

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Speaker 1: I'm just asking questions, Emily.

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Speaker 2: It's a little suss, a little sus. If the kids

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Speaker 2: would say, there's something going on, I don't know what

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Speaker 2: it is.

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Speaker 1: Can I tell you the most genius thing that happened

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Speaker 1: this weekend is we were out celebrating a friend's birthday

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Speaker 1: and it was like a fun night, and we go

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Speaker 1: back to their house at the end of the night

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Speaker 1: and someone's like, let's door dash bunch of taco bell

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Speaker 1: and the theory here was there is no better time

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Speaker 1: to door dash a bunch of talk. This is going

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Speaker 1: to be the healthiest talk of my husband ever.

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Speaker 2: Is as the economist firm of that which he's like,

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Speaker 2: tacobo must be so cheap. Right now, we should go

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Speaker 2: you know how much food you can get for almost

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Speaker 2: nothing and there won't be any lines. Mean, we're thinking,

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Speaker 2: we're thinking the same the time. But guys still avoid

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Speaker 2: iceberg lettus for the moment unless you know where it

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Speaker 2: was grown and it was like your backyard, all right.

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Speaker 2: Next up, aging research is setting upper bounds on our

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Speaker 2: human life span. Disappointing news for Brian Johnson and for

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Speaker 2: the rest of us. But what does this say? How

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Speaker 2: old could I live to be?

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Speaker 1: These studies are entirely theoretical and always so interesting to me.

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Speaker 1: Like people have asked the question, this isn't what this

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Speaker 1: study is about. But people have asked the question, like

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Speaker 1: what if you what if the only thing that could

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Speaker 1: kill you was accidents? Right, like like you're basically a mortal,

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Speaker 1: you're an l or something, but like you can get

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Speaker 1: killed if you fall out of a tree or something

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Speaker 1: like that, but you'll never get sick, you'll never get old.

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Speaker 1: And you can model this mathematically because you know what

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Speaker 1: like the rate of accidents, fatal accidents are And it

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Speaker 1: turns out I'm not going to get exactly right, but

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Speaker 1: it turns out like unaverage, people would live to be

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Speaker 1: seven hundred or eight hundred years old if the only

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Speaker 1: thing that could kill you was getting in an accident. Okay, cool.

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Speaker 1: This study looked at DNA mutations, so as you age

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Speaker 1: your DNA, you know, little gamma rays come from outer

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Speaker 1: space and they mutate DNA, and they asked the question,

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Speaker 1: if this is the only thing that can kill you,

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Speaker 1: are these like random gamma rays? Like we fix everything else, right,

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Speaker 1: there's no heart disease, there's no cancer, but we can't

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Speaker 1: fix the random gamma rays mutating your DNA. What's the

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Speaker 1: maximum you can live? And the overall answer it was

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Speaker 1: one hundred and forty six to one hundred and ninety

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Speaker 1: four years. This was limited mostly by problems in your heart,

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Speaker 1: which the DNA mutation accumulation would cause fatal heart ysfunction

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Speaker 1: at about one hundred and fifty years. Similar levels in

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Speaker 1: the brain. Interestingly, the liver, which is always just like

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Speaker 1: down to party, could go for thousands of years before

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Speaker 1: it would kill you from just DNA mutations alone. And

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Speaker 1: so the researchers are saying, like, Okay, this is like

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Speaker 1: longevity people. You know, this is the upper limit here,

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Speaker 1: like you fix all the other stuff, you know, this

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Speaker 1: is what's going to get you. But of course, like

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Speaker 1: if you fix all the other stuff, I don't know,

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Speaker 1: can't wait fix totally.

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Speaker 2: I mean, this seems ridiculous. I don't know if these

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Speaker 2: people have not watched Futurama where you just put your

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Speaker 2: heads in the in the continue to watch Futurama. That

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Speaker 2: was so great. People just live forever and their heads

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Speaker 2: were like and these like it was just their head

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Speaker 2: in like a little tube or whatever. I don't know

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Speaker 2: how it worked, but it's yeah, yeah, like all the less,

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Speaker 2: so get with it, aging researchers. But but I think

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Speaker 2: it's like, mathematically, this is a super interesting approach. I

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Speaker 2: like it I like it a.

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Speaker 1: Lot of Yeah, that's kind of fun. Let's move on

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Speaker 1: to talk about Secretary of str I mean, I think

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Speaker 1: legally it's still the Secretary of Defense, but whoever he

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Speaker 1: is has a plan to test all I guess nope, male.

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Speaker 2: Sorry, it's to test all people in the military for testosterone,

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Speaker 2: including ladies. Spoiler, they don't have very much.

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Speaker 1: Oh geez, Okay. I love when stories are even stupider

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Speaker 1: than I assume more.

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Speaker 2: Logic than than he was going with this.

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Speaker 1: So I think the overall, at least the overall idea

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Speaker 1: here is that they want to identify people with low

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Speaker 1: testosterone and presumably to supplement it in order to increase

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Speaker 1: the efficacy of the armed forces. If you want to

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Speaker 1: go back in time to our testosterone replacement therapy episode,

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Speaker 1: you can get all the science about why this is

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Speaker 1: a bad idea, including the fact testosterone supplementation doesn't end

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Speaker 1: up doing much for men except increasing libido, and testing

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Speaker 1: testosterone levels is not really recommended unless you're having libido

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Speaker 1: or rectile dysfunction problems. And I don't think that's what

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Speaker 1: the Secretary of War is worried about. God, I hope

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Speaker 1: it's not that.

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Speaker 2: Yeah, I mean I think his you know, I think

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Speaker 2: he's sort of pitching an idea here that's like, I mean,

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Speaker 2: we've seen this, this is Captain America. But I think

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Speaker 2: you know, FDA approved level of testosterone supplementation will not

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Speaker 2: produce Captain America. No, So I'm not really sure where

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Speaker 2: we're going with this issue.

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Speaker 1: I mean, yeah, you could like juice up all the

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Speaker 1: army people. You've give them real steroids, like anabolic steroids,

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Speaker 1: and you know, have them go into roid rage in

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Speaker 1: the battlefield. I'm not really sure that's what we want.

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Speaker 1: I don't know. This feels I don't exactly know how

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Speaker 1: to put this, but this obsession with testosterone and sperm

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Speaker 1: count and stuff feels like this is what my kids

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Speaker 1: would say. It's like small dick energy. Okay, that's what

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Speaker 1: this is.

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Speaker 2: That's what it is like.

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Speaker 1: Just you don't have to prove your manliness constantly by

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Speaker 1: like announcing it to the world and performing it just

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Speaker 1: like be a good dude, Jesus, be a good dude.

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Speaker 2: But consider getting your sperm count and disosterone levels and

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Speaker 2: a small tattoo in a place that you know, just

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Speaker 2: people might see, like on your cheek.

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Speaker 1: Consider the lower back. That's what I want all our army,

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Speaker 1: you know, like that's it, That's what I'm talking about.

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Speaker 2: It's amazing, that's what I want.

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Speaker 1: Let's make that manly, all right. I think with those

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Speaker 1: news items under our belt, let's take a dive into alcohol.

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Speaker 1: And you know, I think we can actually skip this

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Speaker 1: entire episode because someone has summed it up more perfectly

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Speaker 1: than you and I ever could. Emily. Let me play

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Speaker 1: the clip to.

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Speaker 3: Welcome because of and solution to all of life's problems.

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Speaker 2: Homer is very wise. He's wise.

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Speaker 1: This is the best episode if you're a Simpsons fan.

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Speaker 1: The prohibition episode is so great when he becomes the

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Speaker 1: beer baron and Marge is actually proud of him for

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Speaker 1: doing something clever as he's filling bowling balls with hooch,

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Speaker 1: and like there's a whole system of pipes that takes

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Speaker 1: sit from the bowling alley to mose bar. Such a

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Speaker 1: classic alcohol Emily has been around for quite some time.

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Speaker 2: Yeah, people have been fermenting things for a very long time.

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Speaker 2: I think the first confirm fermentation is seven thousand BCE,

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Speaker 2: so that's what nine thousand years ago.

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Speaker 1: That's a long time.

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Speaker 2: I mean, fermentation is not hard, you leave stuff. You

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Speaker 2: could imagine how this happens, right, You make some stuff

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Speaker 2: with rice, you leave it out for a little while,

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Speaker 2: you're thirsty, you taste it, you realize it doesn't taste

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Speaker 2: that great. But boy, is this fun? And then we're

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Speaker 2: kinda off to the races.

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Speaker 1: Yeah. Absolutely, And it's not just humans who do this amazingly.

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Speaker 1: So a brief list of animals who have been documented

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Speaker 1: to actively seek out fermented fruits. So these are animals

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Speaker 1: basically that like, there'll be a you know, fruit rotting

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Speaker 1: essentially becomes alcoholic over time. Here's some animals that like

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Speaker 1: to play around with these chimpanzees, black handed spider monkeys,

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Speaker 1: African elephants, tree shrews, and even fruit flies. Although apparently

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Speaker 1: fruitflies aren't getting drunk. They're laying their eggs in fermented

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Speaker 1: fruit because the antiseptic properties of alcohol prevent some egg

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Speaker 1: diseases on fruitfly eggs. That's nice.

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Speaker 2: So people have been consuming alcohol, either for fun or

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Speaker 2: you know, in some periods, beer was a sort of

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Speaker 2: much safer way to consume liquid than water because it

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Speaker 2: was much less likely to be full of cholera and

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Speaker 2: so on. So we really, alcohol is you know, it's

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Speaker 2: one of our oldest customs. The Romans loved wine. Every

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Speaker 2: everybody loves alcohol.

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Speaker 1: Everybody loves alcohol. Why are they telling us it's so

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Speaker 1: bad for us?

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Speaker 2: Then? I don't know. I guess we're ready to get

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Speaker 2: into that. But it is. It has been popular for

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Speaker 2: many millennia, let's put it that way.

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Speaker 1: On the medical side, a bit of fun history of prohibition.

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Speaker 1: Not that prohibition was that fun. So US Prohibition was

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Speaker 1: from nineteen twenty to nineteen thirty three. The Eighteenth Amendment

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Speaker 1: ban the sale of alcohol at that time, but it

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Speaker 1: did carve out an exception for medicine, and so you

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Speaker 1: could get a prescription for medicinal whiskey. And apparently in

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Speaker 1: that period doctors wrote an estimated eleven million prescriptions for

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Speaker 1: medicinal whiskey, treating ailments ranging from indigestion to depression, usually

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Speaker 1: charging a hefty fee for those prescriptions.

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Speaker 2: What else is new that sounds right? I mean, Prohibition

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Speaker 2: is a very interesting historical episode, obviously, because there were

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Speaker 2: a lot of social problems. There are a lot of

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Speaker 2: social problems caused by alcohol. You can see the instinct,

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Speaker 2: and yet you seem to really backfire in a lot

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Speaker 2: of ways. It was certainly unpopular in addition to generating

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Speaker 2: a lot of mob related crime and apparently fake prescriptions.

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Speaker 1: You banned stuff that is really easy to make in

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Speaker 1: your own home, and people are going to make it

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Speaker 1: in their own home totally.

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Speaker 2: Have you ever made your own alcohol?

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Speaker 1: I have. I brew I've brewed my own beer. That's

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Speaker 1: all I've done. I've never distilled anything, but I had

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Speaker 1: fun brewing my own beer. I made this very delicious

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Speaker 1: tasting IPA that was not very high in alcohol content,

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Speaker 1: which is now like a popular thing.

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Speaker 2: One time in college, one of my roommates brought back

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Speaker 2: raisin based homemade liquor from Christmas break. It was bad. Actually,

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Speaker 2: I wouldn't recommend making hard Linger out of raisins.

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Speaker 1: One thing I used to see in Pennsylvania are people

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Speaker 1: who would come in with chronic lead toxicity, which can

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Speaker 1: present doctors as the triad of kidney disease, gout, and hypertension.

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Speaker 1: And what we would occasionally trace it back to are

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Speaker 1: people making moonshine, because in Appalachia, people will often distill

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Speaker 1: the liquor. The moonshine liquor in old car radiators, which

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Speaker 1: used to be lead lined and so the lead would

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Speaker 1: leach out into the moonshine. Then they drink moonshine. They'd

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Speaker 1: come and see us with gout and kidney disease.

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Speaker 2: Yikes. Okay, all right, so don't make your moonshine and

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Speaker 2: your car radiator more. You know, how does alcohol work,

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Speaker 2: doctor Perry, Let's do it. What are the reasons the

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Speaker 2: alcohol is fun? Basically, that's the first question.

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Speaker 1: Yeah, it works in your brain. It actually works like

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Speaker 1: a lot. We have drugs that kind of work in

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Speaker 1: similar ways, and alcohol sort of touches on all of these.

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Speaker 1: So the primary one that you'll read about is that

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Speaker 1: if it stimulates the GABBA receptor in the brain. This

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Speaker 1: is the same receptor that benzodiazepines like adavan, valuum, those

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Speaker 1: types of drugs bind too, So it gives that sort

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Speaker 1: of tired, sleepy, funky feeling that comes with stimulation of

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Speaker 1: that receptor. But it also blocks the NMDA receptor. That

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Speaker 1: is what ketamine does, so it has a little bit

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Speaker 1: of the depressiant effect or the dissociative effect that you

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Speaker 1: see with ketamine. It causes dopamine release in the brain,

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Speaker 1: which is why it feels pleasant and happy and good

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Speaker 1: at least for most people, and it releases endogenous opioids,

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Speaker 1: so it has even an opioid effect in the brain.

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Speaker 1: So it kind of it's like it's like an amazing

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Speaker 1: cocktail note pun intended of drugs that people like to

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Speaker 1: abuse because it makes their brain feel nice and it

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Speaker 1: also makes you peel lot.

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Speaker 2: Okay, So we talked about prohibition and and post prohibition,

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Speaker 2: Americans have been drinking a lot. But actually in the US,

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Speaker 2: the amount that people drink has been going down very

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Speaker 2: precipitously over time. There was a sort of bump up

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Speaker 2: over the last short period. There was a bump up

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Speaker 2: during COVID wheneveryone was at home drinking, but it has

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Speaker 2: continued a downward slide and we are now seeing both

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Speaker 2: the sort of the lowest levels of drinking, the most

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Speaker 2: number of people who are totally sober, and also a

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Speaker 2: lot of discussion in I would say, the wellness and

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Speaker 2: health space about how much alcohol is okay and more

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Speaker 2: and more a message that none is okay. I would say,

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Speaker 2: the kind of there's a piece of health messaging that

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Speaker 2: comes some of it from the government, some of it

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Speaker 2: from official sources, some of it from online that's in

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Speaker 2: the space of you know, the appropriate amount is really none,

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Speaker 2: and that even a small amount is bad for your health,

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Speaker 2: and how bad is not so clear, and that's the

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Speaker 2: piece of the messaging. I'm most interested in looking at

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Speaker 2: the data on today because I think it's the piece

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Speaker 2: that's most relevant for a lot of people. But before that,

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Speaker 2: I actually do want to touch just briefly on the

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Speaker 2: question of problematic drinking, because these are fundamentally somewhat different questions,

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Speaker 2: And the question of you know, is it okay three

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Speaker 2: times a week to have a glass of wine with

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Speaker 2: dinner is pretty different from you know, how do we

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Speaker 2: think about someone who from whom alcohol is getting in

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Speaker 2: the way of their life or their family's life or

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Speaker 2: other things. But that is an issue for many people.

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Speaker 2: Alcohol is something a lot of people have a problem

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Speaker 2: with and abuse. And it's also very clear in the

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Speaker 2: data that heavy drinking is associated with quite a lot

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Speaker 2: of bad outcomes. There are large estimates of amount of

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Speaker 2: access death from heavy drinking. So I'm not even sure

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Speaker 2: how to separate these, and I think it gets complicated

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Speaker 2: because there's a continuum, it's not just two groups of people.

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Speaker 2: But I do want to try hard in the discussion

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Speaker 2: to sort of separate those two things.

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Speaker 1: Yeah, I couldn't agree more. I think I think most

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Speaker 1: people you know, if you have alcohol use disorder, if

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Speaker 1: your alcohol use is affecting your daily life, if you

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Speaker 1: feel guilty about drinking, if a loved one has brought

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Speaker 1: it up to you that perhaps you're drinking too much,

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Speaker 1: if you need to drink after you wake up in

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Speaker 1: the morning, Like, there are plenty of signals of problematic drinking,

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Speaker 1: and that is a group of people who need to

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Speaker 1: seek some help because it definitely is associated with many

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Speaker 1: bad health outcomes and of course life outcomes. And that's

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Speaker 1: not really what we're talking about today. We're talking to

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Speaker 1: the majority of people who may drink a bit, they

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Speaker 1: drink moderately, who are hearing all these statements that every

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Speaker 1: single glass of wine you have, every single drink is

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Speaker 1: poison and want to know if that's true or not.

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Speaker 1: And that's where we have to break down the data.

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Speaker 2: Yeah, before we leave the question of problematic drinking, this

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Speaker 2: is actually a space where we could do another episode

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Speaker 2: on it. But there's a space where there is an

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Speaker 2: increasingly interesting discussion about different possible treatments. There's been some

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Speaker 2: evidence of gilp ones have improved alcohol use disorder. There's

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Speaker 2: a drug called nltroc zone which people have used, and

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Speaker 2: actually a variety of interesting ways to try to limit

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Speaker 2: alcohol use. So that's for another day, but let me

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Speaker 2: just leave here by saying, if you are struggling with

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Speaker 2: alcohol use disorder, if you feel like alcohol is getting

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Speaker 2: in the way of your life, that really is something

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Speaker 2: to seek treatment for, and it is there are an

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Speaker 2: increasing number of possible solutions that might be helpful.

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Speaker 1: Sounds good for the rest of you. We are going

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Speaker 1: to dig into the data about light to moderate drinking

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Speaker 1: and its health effects if any.

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Speaker 2: After the break, Okay, Perry, we are back and we're

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Speaker 2: going to talk about what the data says on light

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Speaker 2: to moderate drinking. And I want to play you a

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Speaker 2: clip of somebody's feeling about this.

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Speaker 4: It's one of those areas where you don't understand the

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Speaker 4: hidden cost until you really give it up for a while.

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Speaker 4: And then I think about my own relationship with drinking,

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Speaker 4: and I stopped drinking at thirty years old and now

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Speaker 4: thirty three, and I had just drank because I just drank.

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Speaker 4: I'd never ran the experiment of just giving it up

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Speaker 4: for a while, and then like, I don't know, must

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Speaker 4: maybe I was at thirty one. I thought, you know,

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Speaker 4: I have a drink again, because now I could really

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Speaker 4: ab test it. I had a year of not drinking,

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Speaker 4: decided to have a drink again. It ruined three days

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Speaker 4: of my life. I had a couple of glasses of wine,

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Speaker 4: didn't get drunk. It ruined three days of my life

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Speaker 4: because of the domino effected course, sort it meant that

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Speaker 4: I got worse sleep that night, and then because I

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Speaker 4: got worse sleep that night, I more poorly the next

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Speaker 4: day because my my dopamine system or whatever, the quartersole

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Speaker 4: system was all messed up. And then I podcasted worse.

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Speaker 4: I didn't go to the gym the day after that,

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Speaker 4: that day all the day after because of that, because

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Speaker 4: I felt really bad. I then slept worse. And I

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Speaker 4: could track all of this on my week hashtag and

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Speaker 4: hashtag sponsor, hashtag investor whatever. Yeah, And I was like,

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Speaker 4: oh my god, the three glasses of wine had this

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Speaker 4: hidden domino effect that I must have been living with, yeah,

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Speaker 4: for my whole life.

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Speaker 1: I like how one of his metrics was like, oh,

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Speaker 1: I'm not as good at podcasting after I drink, which

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Speaker 1: definitely I was like, oh, no, did you drink last night?

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Speaker 1: I had a gossip of wine last night, or maybe

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Speaker 1: slightly more than that.

435
00:23:08,920 --> 00:23:11,840
Speaker 2: I don't. I don't drink, actually.

436
00:23:12,280 --> 00:23:15,080
Speaker 1: You don't drink alcohol ever what not?

437
00:23:15,280 --> 00:23:18,480
Speaker 2: Ever, I very occasionally drink, and I used to drink more,

438
00:23:18,520 --> 00:23:21,240
Speaker 2: but at this point I maybe like maybe once every

439
00:23:21,280 --> 00:23:22,040
Speaker 2: couple of months.

440
00:23:22,200 --> 00:23:24,040
Speaker 1: I feel like they should have come out earlier in

441
00:23:24,080 --> 00:23:25,600
Speaker 1: the episode. But that's okay.

442
00:23:26,480 --> 00:23:30,119
Speaker 2: I will tell you I more or less quid drinking

443
00:23:30,160 --> 00:23:33,399
Speaker 2: because it really affects my whoop recovery score. It's the

444
00:23:33,440 --> 00:23:35,960
Speaker 2: same reason as every other thing I do. Very Actually,

445
00:23:36,600 --> 00:23:38,720
Speaker 2: it's like twelve percent of my whoop and that's.

446
00:23:38,600 --> 00:23:43,720
Speaker 1: It so amazing. I mean, how do you you know,

447
00:23:43,840 --> 00:23:46,080
Speaker 1: forget your troubles at the end of the day. How

448
00:23:46,080 --> 00:23:46,240
Speaker 1: do you.

449
00:23:46,240 --> 00:23:50,560
Speaker 2: Wildly I just like, look at my whoop recovery score.

450
00:23:50,560 --> 00:23:53,920
Speaker 2: I'm like, okay, I don't, I am it's not something

451
00:23:53,960 --> 00:23:56,840
Speaker 2: I miss. I really like, I really non alcoholic beer.

452
00:23:57,720 --> 00:23:59,640
Speaker 1: Yeah, I don't mind something not alcoholic.

453
00:23:59,359 --> 00:24:01,639
Speaker 2: Bear fan of But yeah, anyway, I don't drink. So

454
00:24:01,880 --> 00:24:04,000
Speaker 2: that's why I'm so much better at podcasting than you.

455
00:24:05,400 --> 00:24:08,439
Speaker 1: I guess that does explain quite a bit. Everyone Emily

456
00:24:08,520 --> 00:24:12,640
Speaker 1: is a teetotaler, and I there's this old joke which is,

457
00:24:13,000 --> 00:24:15,040
Speaker 1: if you ask a doctor what the definition of an

458
00:24:15,040 --> 00:24:17,520
Speaker 1: alcoholic is, the answer is, well, anyone who drinks more

459
00:24:17,560 --> 00:24:18,560
Speaker 1: than I do.

460
00:24:20,720 --> 00:24:24,960
Speaker 2: So so if you ask not a doctor. But the

461
00:24:25,080 --> 00:24:29,640
Speaker 2: US dietary guidelines, we actually have some I would say

462
00:24:29,680 --> 00:24:34,320
Speaker 2: they're fluid guidelines, but we have some US dietary guidelines

463
00:24:34,359 --> 00:24:39,600
Speaker 2: about how much alcohol is considered recommended. And again these

464
00:24:39,640 --> 00:24:41,600
Speaker 2: things are always like, what do you mean recommended, Like

465
00:24:41,640 --> 00:24:44,480
Speaker 2: that's a good amount, that's too lit with. But those

466
00:24:44,600 --> 00:24:47,919
Speaker 2: numbers in the US Dietary Guidelines from twenty twenty to

467
00:24:47,920 --> 00:24:50,399
Speaker 2: twenty twenty five were less than or equal to one

468
00:24:50,440 --> 00:24:53,120
Speaker 2: drink a day for women two drinks a day for men.

469
00:24:54,000 --> 00:24:57,760
Speaker 2: In the twenty twenty six guidelines, they just went with less,

470
00:24:58,400 --> 00:25:00,840
Speaker 2: so I don't know, that's not a number, But the

471
00:25:00,880 --> 00:25:03,320
Speaker 2: twenty six guidelines are a bit different. So just not

472
00:25:03,440 --> 00:25:05,440
Speaker 2: so much is where they want.

473
00:25:05,440 --> 00:25:07,440
Speaker 1: I should have said fewer. I feel like that's okay.

474
00:25:07,480 --> 00:25:07,840
Speaker 3: Fewer.

475
00:25:09,080 --> 00:25:12,159
Speaker 2: The American Heart Association is in the range of like

476
00:25:12,200 --> 00:25:14,880
Speaker 2: two drinks a day probably limited risk. The World Health

477
00:25:14,960 --> 00:25:19,680
Speaker 2: Organization says none, So there is you know, everyone kind

478
00:25:19,680 --> 00:25:22,840
Speaker 2: of agrees that you should keep it low, but people

479
00:25:23,119 --> 00:25:27,600
Speaker 2: differ on what that means or how bad it is.

480
00:25:27,840 --> 00:25:30,719
Speaker 2: And the reason for that is that the data is

481
00:25:32,160 --> 00:25:36,600
Speaker 2: mostly trash. Actually, when we are talking about light drinking.

482
00:25:36,800 --> 00:25:39,240
Speaker 1: Yeah, I think before we dig in, we're going to

483
00:25:39,320 --> 00:25:42,840
Speaker 1: dig into some of the risks cancer, cardiovascular disease, and

484
00:25:42,880 --> 00:25:45,120
Speaker 1: so on in a minute. But to set the stage,

485
00:25:45,520 --> 00:25:48,760
Speaker 1: there is a problem in the literature. There's the standard

486
00:25:48,760 --> 00:25:51,760
Speaker 1: problem of observational data, which we'll get to talk about

487
00:25:51,760 --> 00:25:55,160
Speaker 1: this a lot. But there's another problem which is called

488
00:25:55,240 --> 00:25:57,360
Speaker 1: or one way we can call it is linear extrapolation.

489
00:25:58,119 --> 00:26:02,400
Speaker 1: And that's when you have have an exposure that has

490
00:26:02,440 --> 00:26:05,199
Speaker 1: a dose. So it's not just like yes, no, you

491
00:26:05,240 --> 00:26:08,399
Speaker 1: take a medication, but it has a dose associated with it,

492
00:26:08,800 --> 00:26:11,760
Speaker 1: and you have a wide range of those doses, and

493
00:26:12,000 --> 00:26:14,359
Speaker 1: you're looking at a large data set where some people

494
00:26:14,359 --> 00:26:16,879
Speaker 1: have very high doses of the thing like alcohol, and

495
00:26:16,920 --> 00:26:19,119
Speaker 1: some people are very low, and some people have none.

496
00:26:19,320 --> 00:26:20,960
Speaker 1: And you look at the rates of some outcome and

497
00:26:21,000 --> 00:26:24,560
Speaker 1: you draw a line, You fit a line of best

498
00:26:24,560 --> 00:26:28,679
Speaker 1: fit to all of these data points. And one of

499
00:26:28,680 --> 00:26:31,960
Speaker 1: the risks of doing that is that if the risk

500
00:26:32,240 --> 00:26:36,560
Speaker 1: actually clusters at the high end fitting a line can

501
00:26:36,600 --> 00:26:40,040
Speaker 1: be inappropriate. There can be threshold effects. For example, we

502
00:26:40,080 --> 00:26:42,479
Speaker 1: see this not only with alcohol, but for example, with

503
00:26:43,200 --> 00:26:47,440
Speaker 1: radiation dosages. So a lot of the sort of quote

504
00:26:47,480 --> 00:26:50,040
Speaker 1: unquote risk of getting a chest X right for example,

505
00:26:50,080 --> 00:26:53,240
Speaker 1: which is a vanishingly small amount of radiation, they'll be like, oh,

506
00:26:53,240 --> 00:26:56,480
Speaker 1: it increases your risk of cancer by zero point zero

507
00:26:56,600 --> 00:26:59,840
Speaker 1: zero zero three percent every chest X ray. Well, actually

508
00:27:00,040 --> 00:27:02,879
Speaker 1: that data is coming from the cancer rate of people

509
00:27:02,920 --> 00:27:06,720
Speaker 1: who were like in Hiroshima, Nagasaki and surrounding areas, who

510
00:27:06,760 --> 00:27:09,280
Speaker 1: got you know, thousands of times the doses and then

511
00:27:09,440 --> 00:27:12,679
Speaker 1: a linear extrapolation down to getting one one thousands of

512
00:27:12,720 --> 00:27:15,600
Speaker 1: the dose, and that's just not always accurate in biology.

513
00:27:15,680 --> 00:27:18,040
Speaker 1: So that's a big meta problem we're going to have

514
00:27:18,040 --> 00:27:19,800
Speaker 1: to face with all this stuff. And I think that's

515
00:27:19,800 --> 00:27:22,200
Speaker 1: why the who comes out and says like no level

516
00:27:22,320 --> 00:27:25,440
Speaker 1: is safe, because they're extrapolating. They're extrapolating.

517
00:27:25,840 --> 00:27:30,760
Speaker 2: And then of course we have our standard observational data problem.

518
00:27:31,119 --> 00:27:33,720
Speaker 2: There's a little you know, we'll talk I think maybe

519
00:27:33,720 --> 00:27:35,760
Speaker 2: at a couple of places where you've got a little

520
00:27:35,760 --> 00:27:38,439
Speaker 2: bit of randomized data on sort of small things, but

521
00:27:38,480 --> 00:27:41,480
Speaker 2: for the most part this is going to come from

522
00:27:41,600 --> 00:27:45,520
Speaker 2: data that compares people who drink different amounts and looks

523
00:27:45,600 --> 00:27:51,360
Speaker 2: at their outcomes and tries to adjust for some differences

524
00:27:51,480 --> 00:27:54,520
Speaker 2: across them, but is not going to be able to

525
00:27:54,560 --> 00:27:57,840
Speaker 2: do that in a complete way. And I actually think

526
00:27:58,240 --> 00:27:59,919
Speaker 2: sometimes we talk about this, I think it can be

527
00:28:00,040 --> 00:28:02,199
Speaker 2: quite opaque to people, like what do you mean They

528
00:28:02,200 --> 00:28:04,000
Speaker 2: couldn't adjust for the things like what do you mean

529
00:28:04,040 --> 00:28:08,040
Speaker 2: they couldn't see enough details about about people. But in

530
00:28:08,080 --> 00:28:11,040
Speaker 2: these studies you will see things about people like did

531
00:28:11,119 --> 00:28:13,880
Speaker 2: they go to college, how much income do they have?

532
00:28:14,240 --> 00:28:17,480
Speaker 2: You know, kind of in some broad buckets, but you

533
00:28:17,640 --> 00:28:22,280
Speaker 2: won't see all of the little details. And like, to

534
00:28:22,320 --> 00:28:25,000
Speaker 2: go back to our conversation about our behaviors, you and

535
00:28:25,040 --> 00:28:27,800
Speaker 2: I actually in these studies to this researchers would look

536
00:28:27,880 --> 00:28:31,280
Speaker 2: very similar other than our gender, right, we both we

537
00:28:31,359 --> 00:28:34,040
Speaker 2: actually went to the same college. Forget they never see that,

538
00:28:34,080 --> 00:28:35,879
Speaker 2: but we went to you know, we both went to college.

539
00:28:35,920 --> 00:28:38,680
Speaker 2: We probably are in similar income brackets, we have you know,

540
00:28:38,960 --> 00:28:42,800
Speaker 2: similar other kinds of risk factors. But let's say you

541
00:28:42,800 --> 00:28:46,000
Speaker 2: were running a study where the outcome was like you know,

542
00:28:46,280 --> 00:28:48,720
Speaker 2: some kind of like votumax or some like sort of

543
00:28:48,760 --> 00:28:54,000
Speaker 2: cardiovascular performance when we run that study, you drink more

544
00:28:54,040 --> 00:28:56,760
Speaker 2: than I do, and we're going to see, like that's

545
00:28:56,840 --> 00:28:58,800
Speaker 2: going to if you and I are like representative, it's

546
00:28:58,800 --> 00:29:00,600
Speaker 2: going to look like, boy, not drink is really good

547
00:29:00,600 --> 00:29:03,120
Speaker 2: for your VOTWO max. But actually the reason I don't

548
00:29:03,200 --> 00:29:05,480
Speaker 2: drink is because I'm doing every possible thing in my

549
00:29:05,520 --> 00:29:07,920
Speaker 2: whole life to invest in the VOTWO max, which the

550
00:29:07,960 --> 00:29:11,160
Speaker 2: researchers will never see. And that's the kind of example

551
00:29:11,280 --> 00:29:14,200
Speaker 2: of where like there's so much in the data and

552
00:29:14,280 --> 00:29:18,880
Speaker 2: it's all causing itself that it's really hard to draw conclusions,

553
00:29:19,000 --> 00:29:21,720
Speaker 2: especially when you're talking about you know, what's the impact

554
00:29:21,800 --> 00:29:25,560
Speaker 2: of one drink or two drinks, which where those impacts

555
00:29:25,560 --> 00:29:29,640
Speaker 2: are very very small relative to the size of these biases.

556
00:29:29,920 --> 00:29:31,440
Speaker 1: Yeah, and this is one of the reasons I think

557
00:29:31,560 --> 00:29:36,080
Speaker 1: red wine in particular, for like twenty years was a

558
00:29:36,120 --> 00:29:39,400
Speaker 1: health food to some extent, because there was observational research

559
00:29:39,800 --> 00:29:41,720
Speaker 1: that said, oh, you know, drinking a glass or two

560
00:29:41,760 --> 00:29:43,600
Speaker 1: of red wine today is associated with the lower risk

561
00:29:43,680 --> 00:29:46,120
Speaker 1: of cardiovascular disease and so on and so forth, And

562
00:29:46,200 --> 00:29:49,960
Speaker 1: that is true associationally, like there's a correlation there, But

563
00:29:50,480 --> 00:29:53,840
Speaker 1: who's the type of person who drinks red wine, especially

564
00:29:53,960 --> 00:29:56,960
Speaker 1: versus other types of alcohol. Right, Yeah, it's rich people.

565
00:29:57,480 --> 00:30:00,760
Speaker 1: It's you know, people who eat a very different diet

566
00:30:00,840 --> 00:30:03,560
Speaker 1: than people who are drinking even beer or hard alcohol

567
00:30:03,600 --> 00:30:05,640
Speaker 1: or things like that. And we sort of know this

568
00:30:05,720 --> 00:30:10,320
Speaker 1: is BS because the explanation outside of confounding was, oh, no,

569
00:30:10,400 --> 00:30:13,040
Speaker 1: red wine has these stuff in it, like recept stalls

570
00:30:13,120 --> 00:30:17,360
Speaker 1: extre which is this special thing, and that's actually what's

571
00:30:17,400 --> 00:30:19,440
Speaker 1: protecting your heart. And then they did a randomized child

572
00:30:19,440 --> 00:30:20,800
Speaker 1: They're like, well, you don't need to the red wine.

573
00:30:20,880 --> 00:30:24,560
Speaker 1: Let's just synthesize recever trawl and we'll give half the

574
00:30:24,600 --> 00:30:26,479
Speaker 1: people that in half placebo and we'll see what their

575
00:30:26,480 --> 00:30:29,040
Speaker 1: heart attack rate is and bupkus, nothing whatsoever.

576
00:30:29,600 --> 00:30:32,040
Speaker 2: But I actually think that's a particularly good example of

577
00:30:32,080 --> 00:30:34,760
Speaker 2: yet another problem here, which is we started telling people

578
00:30:34,760 --> 00:30:36,680
Speaker 2: in the nineteen seventies red wine is good for you,

579
00:30:37,000 --> 00:30:39,040
Speaker 2: and then who are the people who started drinking red wine?

580
00:30:39,120 --> 00:30:40,600
Speaker 2: Is the people who are like, oh, I'm really interested

581
00:30:40,640 --> 00:30:42,400
Speaker 2: investing in my health, I'm doing all this other stuff,

582
00:30:42,400 --> 00:30:44,280
Speaker 2: I'm going to add some red wine. Then you come

583
00:30:44,280 --> 00:30:46,240
Speaker 2: back to look at them later, it looks like red

584
00:30:46,280 --> 00:30:49,960
Speaker 2: wine is even better because now it's even more selected

585
00:30:50,160 --> 00:30:53,640
Speaker 2: so right right, anyway, this you know, you know how

586
00:30:53,680 --> 00:30:55,520
Speaker 2: I feel about selection vibe.

587
00:30:55,520 --> 00:30:57,400
Speaker 1: I know, but don't worry because I have a Mendelian

588
00:30:57,440 --> 00:31:02,480
Speaker 1: randomization loiety and deep cut for podcast listeners. We know

589
00:31:02,520 --> 00:31:03,360
Speaker 1: how much about my.

590
00:31:03,440 --> 00:31:06,840
Speaker 2: Ex boyfriends in the studies here, all right.

591
00:31:06,760 --> 00:31:09,640
Speaker 1: Let's talk about cancer. That's the elephant in the room.

592
00:31:09,800 --> 00:31:12,480
Speaker 1: I think these days when we're talking about alcohol, and

593
00:31:12,600 --> 00:31:15,600
Speaker 1: I will say from the doctor perspective that there's biologic

594
00:31:15,640 --> 00:31:20,720
Speaker 1: plausibility here. Alcohol is metabolized in primarily in the liver,

595
00:31:20,800 --> 00:31:25,240
Speaker 1: but also in the gut to acid aldehyde. Acid aldehyde

596
00:31:25,920 --> 00:31:30,200
Speaker 1: damages DNA. It does. It is a DNA toxin. We

597
00:31:30,360 --> 00:31:33,160
Speaker 1: know that that happens if you put outset aldehyde on

598
00:31:33,320 --> 00:31:36,560
Speaker 1: cells in a petri dish. Therefore, if you have that

599
00:31:36,600 --> 00:31:38,520
Speaker 1: floating around your body for too long, maybe it can

600
00:31:38,600 --> 00:31:42,480
Speaker 1: damage DNA. Damaged DNA leads to cancer. It's so facto

601
00:31:42,880 --> 00:31:44,600
Speaker 1: alcohol causes cancer.

602
00:31:45,000 --> 00:31:48,280
Speaker 2: And in the observational data, you know, if you look

603
00:31:48,320 --> 00:31:52,360
Speaker 2: at like you fit a trend to the entire distribution

604
00:31:52,400 --> 00:31:56,680
Speaker 2: I'm drinking, you definitely see higher cancer rates with heavier drinking.

605
00:31:57,600 --> 00:32:01,479
Speaker 2: But when you try to limit down to smaller amounts

606
00:32:01,480 --> 00:32:04,320
Speaker 2: of drinking, even in these data, which again have these

607
00:32:04,360 --> 00:32:09,560
Speaker 2: other biases. The effects are either zero or really really

608
00:32:10,120 --> 00:32:13,640
Speaker 2: very tiny, and for example, a lot of these find

609
00:32:13,880 --> 00:32:17,680
Speaker 2: kind of almost no increased risk for people who are

610
00:32:17,720 --> 00:32:21,600
Speaker 2: saying not smokers, and that suggests that maybe some behaviors

611
00:32:21,720 --> 00:32:24,240
Speaker 2: like smoking are also driving some of what we're seeing

612
00:32:24,320 --> 00:32:27,560
Speaker 2: in the relationship, because those seem to move together.

613
00:32:27,760 --> 00:32:33,200
Speaker 1: So yep, absolutely, there's the confounding of smoking, which is

614
00:32:33,240 --> 00:32:35,840
Speaker 1: that when people who drink are more likely to smoke,

615
00:32:35,880 --> 00:32:38,000
Speaker 1: and even when people drink, they might be more likely

616
00:32:38,000 --> 00:32:40,720
Speaker 1: to smoke, even if they're not sort of chronic smokers.

617
00:32:40,960 --> 00:32:44,560
Speaker 1: That's an issue. There is also likely some synergy here

618
00:32:44,760 --> 00:32:49,520
Speaker 1: where the cancer promoting effects of smoke and the potential

619
00:32:49,600 --> 00:32:52,360
Speaker 1: cancer promoting effects of alcohol sort of work together to

620
00:32:52,440 --> 00:32:55,800
Speaker 1: increase the risk more dramatically. I think, Emily, you're referring

621
00:32:55,840 --> 00:32:58,560
Speaker 1: to this big study of two hundred thousand health professionals

622
00:32:58,600 --> 00:33:01,520
Speaker 1: in the US and the BMJ in fifteen, which basically

623
00:33:01,560 --> 00:33:06,360
Speaker 1: showed that across kind of reasonable amounts of consumption, that

624
00:33:06,400 --> 00:33:10,000
Speaker 1: there is no increased cancer risk among non smokers. There's

625
00:33:10,040 --> 00:33:12,160
Speaker 1: an exception to this, which is breast cancer in women,

626
00:33:12,240 --> 00:33:15,600
Speaker 1: and this is one that keeps coming up so it's

627
00:33:15,680 --> 00:33:18,640
Speaker 1: very clear that alcohol doesn't increase the risk of all cancers.

628
00:33:19,000 --> 00:33:22,280
Speaker 1: Even in the bad observational data. The cancers that are

629
00:33:22,320 --> 00:33:25,920
Speaker 1: associated with alcohol intake mostly kind of lie in the

630
00:33:25,960 --> 00:33:28,320
Speaker 1: GI trap, like the area of your body that gets

631
00:33:28,320 --> 00:33:32,120
Speaker 1: exposed to the alcohol, so mouth and oral cancer, stomach cancer,

632
00:33:32,240 --> 00:33:36,160
Speaker 1: colon cancer. But then breast cancer always stands out as

633
00:33:36,440 --> 00:33:40,680
Speaker 1: a signal that we continue to see. So I am

634
00:33:40,680 --> 00:33:43,400
Speaker 1: a little bit more worried about that. But you've heard

635
00:33:43,440 --> 00:33:45,200
Speaker 1: that my wife is a breast cancer surgeon, so that

636
00:33:45,240 --> 00:33:47,240
Speaker 1: one always is the monkey on my back.

637
00:33:47,320 --> 00:33:49,360
Speaker 2: And I will say, even you know, in those the

638
00:33:50,000 --> 00:33:53,120
Speaker 2: kind of size of the impact at small levels or

639
00:33:53,160 --> 00:33:55,120
Speaker 2: drinking is very small. And I guess this gets to

640
00:33:55,200 --> 00:33:57,920
Speaker 2: another piece of this, which is which we can sort

641
00:33:57,960 --> 00:34:00,280
Speaker 2: of talk about it maybe more at the end. I

642
00:34:00,280 --> 00:34:02,480
Speaker 2: think it's a it's a broader picture. But even the

643
00:34:02,520 --> 00:34:05,480
Speaker 2: places where people would say, you know, maybe there's some

644
00:34:05,640 --> 00:34:08,600
Speaker 2: hint of an increase in breast cancer, it's really really

645
00:34:09,280 --> 00:34:10,000
Speaker 2: really small.

646
00:34:10,320 --> 00:34:13,600
Speaker 1: Yeah, And it is not the major risk factor for

647
00:34:13,640 --> 00:34:14,280
Speaker 1: breast cancer.

648
00:34:14,560 --> 00:34:16,480
Speaker 2: It's not the major risk factor for breast cancer. And

649
00:34:16,520 --> 00:34:18,239
Speaker 2: I think we want to think about, you know, like

650
00:34:18,320 --> 00:34:21,000
Speaker 2: that trades off against say the fact that you might

651
00:34:21,080 --> 00:34:24,440
Speaker 2: enjoy this, And this is surrounds all of this conversation

652
00:34:24,520 --> 00:34:27,799
Speaker 2: that we're kind of looking for evidence that alcohol is

653
00:34:27,840 --> 00:34:30,640
Speaker 2: like somehow good for you, as opposed to saying, you know,

654
00:34:30,680 --> 00:34:32,000
Speaker 2: there are a lot of things we do that are

655
00:34:32,040 --> 00:34:34,960
Speaker 2: not health investments and that maybe even have some small

656
00:34:35,000 --> 00:34:38,040
Speaker 2: negative impacts that we like, and that's you know, yeah,

657
00:34:38,200 --> 00:34:41,120
Speaker 2: there's other reasons that people engage in these activities other

658
00:34:41,200 --> 00:34:42,279
Speaker 2: than that they're a health food.

659
00:34:42,680 --> 00:34:44,680
Speaker 1: Yeah, and I did. I worked pretty hard. We'll get

660
00:34:44,719 --> 00:34:47,360
Speaker 1: there to try to find actual hard data that alcohol

661
00:34:47,440 --> 00:34:49,960
Speaker 1: is good for you. We'll get there in a minute.

662
00:34:49,960 --> 00:34:53,160
Speaker 2: But I will say, like another place that that Like

663
00:34:53,280 --> 00:34:56,040
Speaker 2: in this BMJ study, one of the things you see,

664
00:34:56,120 --> 00:34:58,680
Speaker 2: for example, is people who drink a little bit relative

665
00:34:58,680 --> 00:35:00,400
Speaker 2: to not at all, which is kind of the parison

666
00:35:00,440 --> 00:35:02,239
Speaker 2: we're looking at, are actually quite a bit more likely

667
00:35:02,280 --> 00:35:07,080
Speaker 2: to have had a mammogram. So that one interpretation of

668
00:35:07,120 --> 00:35:08,880
Speaker 2: some of this is maybe some of the cancer that

669
00:35:08,920 --> 00:35:13,200
Speaker 2: we're seeing is actually like screening and not actual diagnosis,

670
00:35:13,200 --> 00:35:15,320
Speaker 2: which again gets into the hole like it's just really

671
00:35:15,320 --> 00:35:18,759
Speaker 2: really hard to interpret observational data when you're seeing such

672
00:35:18,840 --> 00:35:21,160
Speaker 2: small effects because a billion different things in the data

673
00:35:21,239 --> 00:35:22,839
Speaker 2: billion different differences might drive them.

674
00:35:23,480 --> 00:35:26,880
Speaker 1: Absolutely. Let's try to get away from observational data. So

675
00:35:26,960 --> 00:35:33,120
Speaker 1: I'm going to talk to you about a Mendelian randomization study. Okay,

676
00:35:33,280 --> 00:35:35,640
Speaker 1: let's do the one that appeared in Cancer in twenty

677
00:35:35,680 --> 00:35:37,759
Speaker 1: twenty two. So for those of you who weren't here

678
00:35:37,760 --> 00:35:41,440
Speaker 1: the last time when Emily, let's just say where it

679
00:35:41,480 --> 00:35:44,440
Speaker 1: got heated about this study design. The idea of Mendelian

680
00:35:44,480 --> 00:35:48,520
Speaker 1: randomization is that in this case, some people are genetically

681
00:35:48,560 --> 00:35:52,200
Speaker 1: predisposed to drink more alcohol than others. We know that

682
00:35:52,200 --> 00:35:55,520
Speaker 1: there are certain gene variants that just meet and that

683
00:35:55,680 --> 00:35:58,279
Speaker 1: mean all else being equal, you're going to drink a

684
00:35:58,360 --> 00:36:00,719
Speaker 1: little bit more. Maybe it hits you in the right way,

685
00:36:00,760 --> 00:36:04,439
Speaker 1: it's more pleasurable. You all might know some people who

686
00:36:04,800 --> 00:36:08,440
Speaker 1: actually genetically almost can't drink alcohol. You know, many people

687
00:36:08,440 --> 00:36:14,000
Speaker 1: of Asian ancestry lack the aldehyde dehydrogenase activity to appropriately

688
00:36:14,040 --> 00:36:16,480
Speaker 1: process alcohol, and it becomes very unpleasant to drink even

689
00:36:16,520 --> 00:36:18,920
Speaker 1: small amounts of alcohol. So there are genetic determinants of

690
00:36:18,920 --> 00:36:21,440
Speaker 1: how much alcohol you take. In this study in Cancer

691
00:36:21,440 --> 00:36:24,760
Speaker 1: in twenty twenty two looked at breast cancer risk. Again,

692
00:36:24,840 --> 00:36:28,160
Speaker 1: something I'm concerned about when it comes to alcohol intake,

693
00:36:28,640 --> 00:36:34,480
Speaker 1: and the model that predicted alcohol intake full stop showed

694
00:36:34,520 --> 00:36:37,279
Speaker 1: that people genetically predisposed to drink more alcohol over their

695
00:36:37,280 --> 00:36:40,920
Speaker 1: lives had no higher risk of breast cancer. But another

696
00:36:41,000 --> 00:36:44,120
Speaker 1: model which looked at the genetic risk of problematic drinking,

697
00:36:44,320 --> 00:36:48,160
Speaker 1: which is a separate genetic risk model, did show an

698
00:36:48,200 --> 00:36:51,960
Speaker 1: increase risk of breast cancer. And so one possible interpretation

699
00:36:52,200 --> 00:36:56,000
Speaker 1: of that could be that, you know, load to moderate

700
00:36:56,160 --> 00:36:59,239
Speaker 1: amounts of alcohol intake don't increase your risk of breast

701
00:36:59,239 --> 00:37:01,960
Speaker 1: cancer equal, but problematic intake does.

702
00:37:03,239 --> 00:37:06,439
Speaker 2: Okay, I guess, I mean sure, I guess that could

703
00:37:06,440 --> 00:37:06,960
Speaker 2: be an introvert.

704
00:37:07,040 --> 00:37:09,040
Speaker 1: Okay, let's move on before Emily says anything else.

705
00:37:09,520 --> 00:37:14,120
Speaker 2: I hate this method. I've please just I can't do

706
00:37:14,200 --> 00:37:17,359
Speaker 2: it again. But I think that there are a lot

707
00:37:17,400 --> 00:37:19,920
Speaker 2: of other things that these genetic variants might be linked to.

708
00:37:20,120 --> 00:37:23,160
Speaker 2: And I don't really think this is any better than

709
00:37:23,960 --> 00:37:26,719
Speaker 2: anything else. But I will say that it is not

710
00:37:26,840 --> 00:37:29,560
Speaker 2: inconsistent with this broader picture that maybe at high levels,

711
00:37:29,640 --> 00:37:32,760
Speaker 2: problematic drinking could be linked with a higher risk of cancer.

712
00:37:32,800 --> 00:37:36,040
Speaker 2: I'm not sure we learn anything particular from this particular

713
00:37:36,480 --> 00:37:39,879
Speaker 2: stupid technology. All right, that's I'm going to leave it there.

714
00:37:40,080 --> 00:37:42,800
Speaker 1: Bottom line cancer if you drink, don't smoke.

715
00:37:43,239 --> 00:37:45,120
Speaker 2: My husband told me last week it was like too

716
00:37:45,200 --> 00:37:49,719
Speaker 2: much Medellia completely, So I'm just gonna I'm just gonna stop.

717
00:37:50,520 --> 00:37:54,640
Speaker 1: Okay, please see prior episode for Emily's rant. All right,

718
00:37:54,840 --> 00:37:58,279
Speaker 1: alcohol maybe modest effects on cancer. We're not too worried

719
00:37:58,280 --> 00:38:01,200
Speaker 1: about load of matter intake. Don't smoke while you are drinking.

720
00:38:01,719 --> 00:38:04,800
Speaker 2: Smoke, don't smoke. Oh yeah, cooking is associated with cancer.

721
00:38:04,840 --> 00:38:07,759
Speaker 1: Don't smoke, right, right, right, sorry, don't smoke at all. Okay, smoke,

722
00:38:07,800 --> 00:38:11,400
Speaker 1: but also don't smoke while're drinking. Even just the one.

723
00:38:12,320 --> 00:38:15,680
Speaker 1: Let's talk about heart disease. I think it actually back

724
00:38:15,680 --> 00:38:18,520
Speaker 1: in the day that was the bigger sort of both conservative.

725
00:38:18,560 --> 00:38:21,520
Speaker 1: First of all, is like is it cardioprotective? Then you're right,

726
00:38:21,600 --> 00:38:22,719
Speaker 1: is it good for your heart? And then is it

727
00:38:22,760 --> 00:38:27,359
Speaker 1: bad for your heart? One slam dunk effect I will

728
00:38:27,360 --> 00:38:30,760
Speaker 1: give you for alcohol on the heart is blood pressure. Totally.

729
00:38:31,080 --> 00:38:33,960
Speaker 1: We know from randomized trials. You take people in the lab,

730
00:38:34,120 --> 00:38:36,280
Speaker 1: you give them alcohol, or you give them place Ebo

731
00:38:36,320 --> 00:38:42,400
Speaker 1: alcohol and their blood pressure goes up. But this effect

732
00:38:42,600 --> 00:38:48,319
Speaker 1: is small. So one meta analysis I was looking at

733
00:38:48,400 --> 00:38:51,759
Speaker 1: that integrated these results from randomized trials and the journal Hypertension.

734
00:38:51,760 --> 00:38:54,239
Speaker 1: This is sort of an older study, but I mean

735
00:38:54,239 --> 00:38:56,879
Speaker 1: this has been study for a long time. One drink

736
00:38:57,000 --> 00:39:00,840
Speaker 1: less per day led to about a one millimeters of

737
00:39:00,840 --> 00:39:05,080
Speaker 1: mercury lower systolic blood pressure. That's the top numbers. So

738
00:39:05,320 --> 00:39:08,160
Speaker 1: I believe this is true, but the effect is not huge.

739
00:39:08,200 --> 00:39:10,560
Speaker 1: That being said, if you're struggling with high blood pressure,

740
00:39:10,840 --> 00:39:12,200
Speaker 1: alcohol ain't helping.

741
00:39:12,920 --> 00:39:14,560
Speaker 2: Yeah, I mean, I will say when I look when

742
00:39:14,600 --> 00:39:16,960
Speaker 2: you dig into sort of some of the studies that

743
00:39:17,040 --> 00:39:21,560
Speaker 2: make this up, they are mostly randomized trials that start

744
00:39:21,560 --> 00:39:24,160
Speaker 2: with a population that's drinking quite a lot and then

745
00:39:24,239 --> 00:39:26,840
Speaker 2: have to sort of encourage comes to drink to drink less.

746
00:39:26,880 --> 00:39:28,600
Speaker 2: So like in one of these studies, you know, average

747
00:39:28,640 --> 00:39:32,040
Speaker 2: consumption at the start is like thirty six strenths a week.

748
00:39:32,920 --> 00:39:34,640
Speaker 2: That's a lot. I mean, that's more than that's more

749
00:39:34,680 --> 00:39:35,400
Speaker 2: than five rimes.

750
00:39:35,280 --> 00:39:35,600
Speaker 1: More than me.

751
00:39:35,800 --> 00:39:39,840
Speaker 2: Like, that's just barely and so I think there's a

752
00:39:40,280 --> 00:39:43,640
Speaker 2: There again, is a little bit of this interpolation issue,

753
00:39:43,800 --> 00:39:47,200
Speaker 2: which is it's less clear if you are drinking one

754
00:39:47,280 --> 00:39:49,680
Speaker 2: drink every day, if going down to zero would actually

755
00:39:49,680 --> 00:39:52,319
Speaker 2: have an impact on your blood pressure. That would be

756
00:39:52,680 --> 00:39:54,680
Speaker 2: that would be measurable, but it is worth saying, you

757
00:39:54,680 --> 00:39:56,640
Speaker 2: know again, if you are struggling with this, it's something

758
00:39:56,960 --> 00:39:59,760
Speaker 2: something to try based on that data.

759
00:40:00,080 --> 00:40:03,080
Speaker 1: It is as nephrologists like me kidney doctors deal with

760
00:40:03,360 --> 00:40:06,680
Speaker 1: complex hypertension. That's one of our sub sub specialties, and

761
00:40:06,719 --> 00:40:09,319
Speaker 1: it's always on our checklist. Like, oh, you know, drink

762
00:40:09,400 --> 00:40:09,960
Speaker 1: less alcohol.

763
00:40:10,280 --> 00:40:14,600
Speaker 2: Heart disease in general has the J shaped curve. Drinking

764
00:40:14,920 --> 00:40:18,000
Speaker 2: a little bit. J shaped curves love it because there's

765
00:40:18,000 --> 00:40:22,520
Speaker 2: so much okay J shaped curve where, which means that

766
00:40:22,760 --> 00:40:24,840
Speaker 2: the people with the lowest risk of heart disease are

767
00:40:24,880 --> 00:40:30,279
Speaker 2: people who drink some but not very much or very little. Now,

768
00:40:30,360 --> 00:40:33,680
Speaker 2: of course, people who drink a little bit are kind

769
00:40:33,680 --> 00:40:35,640
Speaker 2: of different from both the people who don't drink in

770
00:40:35,680 --> 00:40:38,279
Speaker 2: all of the people who drink a lot in ways

771
00:40:38,280 --> 00:40:40,879
Speaker 2: that are observable and ways that are not observable, And

772
00:40:40,960 --> 00:40:44,080
Speaker 2: so it has been very difficult to figure out whether

773
00:40:44,120 --> 00:40:47,680
Speaker 2: this J shaped relationship is a real thing or just

774
00:40:48,400 --> 00:40:51,759
Speaker 2: an artifact of observational bias.

775
00:40:52,000 --> 00:40:55,120
Speaker 1: It's the second yeah, and it's the J shaped curve

776
00:40:55,160 --> 00:40:57,560
Speaker 1: that led to a lot of that one to two

777
00:40:57,640 --> 00:41:00,200
Speaker 1: drinks a day is heart healthy. There's just an other

778
00:41:00,239 --> 00:41:03,160
Speaker 1: Simpsons quote where oh my god, another great episode where

779
00:41:03,200 --> 00:41:08,279
Speaker 1: Homer gets hired by Hank Scorpio and their life is

780
00:41:08,360 --> 00:41:10,960
Speaker 1: just perfect there, but the perfection of their life sort

781
00:41:10,960 --> 00:41:14,480
Speaker 1: of gives them on Nui and Marge. You see her

782
00:41:14,560 --> 00:41:17,239
Speaker 1: drinking and eventually they tell Homer that they can't live

783
00:41:17,239 --> 00:41:20,000
Speaker 1: in this beautiful place anymore. And Marge is like, I'm

784
00:41:20,080 --> 00:41:22,600
Speaker 1: drinking half a glass of wine a day. I know

785
00:41:22,680 --> 00:41:24,560
Speaker 1: the doctors say you're supposed to drink a glass, but

786
00:41:24,600 --> 00:41:29,919
Speaker 1: I just can't drink that much. So, you know, anyway, Sorry,

787
00:41:30,560 --> 00:41:32,600
Speaker 1: that's what the doctors say. And it's this j shape

788
00:41:32,640 --> 00:41:35,799
Speaker 1: curve that led to that belief. But one thing that

789
00:41:36,520 --> 00:41:38,759
Speaker 1: sounds simple, but people really didn't account for in the

790
00:41:38,800 --> 00:41:43,279
Speaker 1: early observational studies that people with chronic health conditions might

791
00:41:43,320 --> 00:41:47,240
Speaker 1: be avoiding alcohol because they make their chronic health conditions worse.

792
00:41:47,800 --> 00:41:52,080
Speaker 1: You know, people with cancer undergoing therapy are going to

793
00:41:52,160 --> 00:41:55,879
Speaker 1: drink less than people who aren't, And so you get

794
00:41:55,920 --> 00:41:58,680
Speaker 1: this tale where non drinkers have a higher risk of

795
00:41:58,800 --> 00:41:59,919
Speaker 1: a lot of different things.

796
00:42:00,320 --> 00:42:02,760
Speaker 2: Yeah, that is called reverse causality. We have a different

797
00:42:02,840 --> 00:42:05,680
Speaker 2: name for them. And yeah, so that shows up there,

798
00:42:05,680 --> 00:42:08,280
Speaker 2: and I think over time, people have kind of moved

799
00:42:08,760 --> 00:42:10,960
Speaker 2: interesting aspect for me of this whole discussion is we

800
00:42:11,040 --> 00:42:13,839
Speaker 2: had this J shape curve, and over time we've kind

801
00:42:13,840 --> 00:42:17,839
Speaker 2: of moved away from the J shape curve into sort

802
00:42:17,880 --> 00:42:22,759
Speaker 2: of arguing that this positive effect is gone and so

803
00:42:22,840 --> 00:42:27,880
Speaker 2: it's really just only the increase. But somehow many people

804
00:42:27,880 --> 00:42:29,960
Speaker 2: who are making that argument are making it like, well,

805
00:42:30,000 --> 00:42:31,680
Speaker 2: the people who are not drinking at all are totally

806
00:42:31,719 --> 00:42:33,640
Speaker 2: different from the people who drink a little bit, But

807
00:42:33,680 --> 00:42:36,040
Speaker 2: those people are then unable to understand the argument that

808
00:42:36,080 --> 00:42:38,359
Speaker 2: the people who drink a lot are also different. It's

809
00:42:38,400 --> 00:42:41,960
Speaker 2: like we're only interested in understanding bias in support of

810
00:42:42,160 --> 00:42:44,440
Speaker 2: view that we are hoping to hold right.

811
00:42:44,320 --> 00:42:48,680
Speaker 1: Right right. For what it's worth, I did pull a

812
00:42:48,760 --> 00:42:53,440
Speaker 1: Mendelian rantimization article Circulation Genomic and Precision Medicine twenty twenty

813
00:42:53,480 --> 00:42:56,800
Speaker 1: which looked at cardiovascular disease based on genetic predisposition to

814
00:42:56,840 --> 00:43:01,800
Speaker 1: alcohol intake and basically found more or less bup kiss.

815
00:43:02,239 --> 00:43:06,080
Speaker 1: They did find higher risk of stroke in people who

816
00:43:06,160 --> 00:43:09,279
Speaker 1: were genetically predisposed to alcohol intake and higher risk of

817
00:43:09,320 --> 00:43:13,640
Speaker 1: peripheral arterial disease, but most of the other signals were

818
00:43:13,680 --> 00:43:16,880
Speaker 1: pretty weak in that analysis. But no protection, certainly no

819
00:43:17,320 --> 00:43:18,320
Speaker 1: heart health signal.

820
00:43:18,480 --> 00:43:21,600
Speaker 2: Yeah, I don't think I don't think it's reasonable to say,

821
00:43:21,680 --> 00:43:24,760
Speaker 2: based on the data that alcohol protects you from heart disease.

822
00:43:24,960 --> 00:43:28,440
Speaker 2: I think there's also pretty limited evidence that at moderate

823
00:43:28,520 --> 00:43:30,080
Speaker 2: levels it is bad for you.

824
00:43:30,520 --> 00:43:31,759
Speaker 1: Yeah, bad for your heart.

825
00:43:32,960 --> 00:43:35,520
Speaker 2: Okay, let's talk about a couple of things where I

826
00:43:35,560 --> 00:43:38,480
Speaker 2: think this we maybe have better evidence that there are

827
00:43:38,480 --> 00:43:43,920
Speaker 2: some impacts. So one is anxiety and depression, which alcohol

828
00:43:44,000 --> 00:43:47,400
Speaker 2: use disorder in particular really makes quite a lot worse.

829
00:43:47,400 --> 00:43:48,839
Speaker 2: And we have a lot of we have a lot

830
00:43:48,880 --> 00:43:49,560
Speaker 2: of evidence on that.

831
00:43:49,960 --> 00:43:52,480
Speaker 1: Yeah, it's just not this is not a treatment for anxiety.

832
00:43:52,520 --> 00:43:55,000
Speaker 1: I know people think it is, and there's a lot

833
00:43:55,040 --> 00:43:58,240
Speaker 1: of self medication going on with alcohol and anxiety and depression,

834
00:43:58,320 --> 00:44:02,000
Speaker 1: but there's no evidence that people with those clinical diagnoses

835
00:44:02,040 --> 00:44:04,040
Speaker 1: that alcohol has any benefit. In fact, there's quite a

836
00:44:04,040 --> 00:44:06,359
Speaker 1: lot of evidence that it makes it worse. That ain't

837
00:44:06,360 --> 00:44:09,799
Speaker 1: the solution. Now, this is different than alcohol acting as

838
00:44:09,840 --> 00:44:13,399
Speaker 1: a social lubricant. We're not talking about just kind of

839
00:44:13,719 --> 00:44:15,520
Speaker 1: I get a little anxious when I meet new people.

840
00:44:15,560 --> 00:44:18,200
Speaker 1: We're talking about actual anxiety depression. But I just want

841
00:44:18,239 --> 00:44:20,120
Speaker 1: to put it out there that this is not we're

842
00:44:20,160 --> 00:44:21,560
Speaker 1: not going to see any benefit. We're only going to

843
00:44:21,560 --> 00:44:23,279
Speaker 1: see harm with alcohol in those conditions.

844
00:44:24,120 --> 00:44:27,000
Speaker 2: The other place I think is we're talking about is sleep.

845
00:44:27,480 --> 00:44:31,839
Speaker 2: I think there's actually very little question that alcohol, even

846
00:44:31,880 --> 00:44:36,560
Speaker 2: at relatively low levels, makes people sleep worse. Yeah, so

847
00:44:36,840 --> 00:44:40,680
Speaker 2: people have you know, like it increases your resting heart rate,

848
00:44:40,719 --> 00:44:44,319
Speaker 2: it lowers the quality of your sleep. And this is

849
00:44:44,400 --> 00:44:47,920
Speaker 2: true for healthy people even with again sort of relatively

850
00:44:48,640 --> 00:44:52,399
Speaker 2: mild amounts of alcohol consumption. So you know, I think

851
00:44:52,719 --> 00:44:54,439
Speaker 2: this is the kind of thing that you can get

852
00:44:54,480 --> 00:44:57,279
Speaker 2: into with the wellness. Like, you know, it made my

853
00:44:57,320 --> 00:45:00,319
Speaker 2: resting heart rate three units hire and tiers. All of

854
00:45:00,320 --> 00:45:02,800
Speaker 2: the problems with that, it's not really clear why. Again,

855
00:45:02,840 --> 00:45:06,600
Speaker 2: sort of from a longevity or clinical meaningful health perspective,

856
00:45:06,680 --> 00:45:09,680
Speaker 2: that matters. I will say, if you are looking to

857
00:45:09,719 --> 00:45:15,240
Speaker 2: optimize tomorrow's performance in some you know, podcast or sporting event,

858
00:45:16,200 --> 00:45:19,359
Speaker 2: you know it is perhaps not the best active Most

859
00:45:19,400 --> 00:45:22,400
Speaker 2: people do not drink before a race. I will say that.

860
00:45:22,920 --> 00:45:27,120
Speaker 1: Yeah, So give me some feedback everyone after this episode,

861
00:45:27,960 --> 00:45:34,040
Speaker 1: how my performance, my performance was. I think it's worth

862
00:45:34,160 --> 00:45:38,640
Speaker 1: mentioning that, yes, illlcohol clearly disrupts sleep. It also has

863
00:45:38,680 --> 00:45:41,520
Speaker 1: an effect of suppressing rem sleep, which is dreaming sleep

864
00:45:41,760 --> 00:45:46,040
Speaker 1: that's through that Gabba receptor antagonism, So benzodiazepines have the

865
00:45:46,040 --> 00:45:51,040
Speaker 1: same effect. In fact, psychiatrists will use benzodiazepines to treat

866
00:45:51,040 --> 00:45:55,200
Speaker 1: people who have chronic nightmares like PTSD induced nightmares and

867
00:45:55,200 --> 00:45:58,400
Speaker 1: things like that, to suppress the dreams. But in general,

868
00:45:58,440 --> 00:46:00,080
Speaker 1: you don't want to suppress your rem sleep. That's a

869
00:46:00,160 --> 00:46:03,680
Speaker 1: lot of memory consolidation happens and learning happens, and so

870
00:46:04,200 --> 00:46:09,040
Speaker 1: that's yet another reason to limit alcohol, especially right before bed.

871
00:46:09,640 --> 00:46:11,920
Speaker 2: I think a lot of people, in the case of

872
00:46:11,920 --> 00:46:14,440
Speaker 2: alcohol use disorder. It is common for people to use

873
00:46:14,480 --> 00:46:16,600
Speaker 2: alcohol to fall asleep, and then they're often up in

874
00:46:16,640 --> 00:46:18,759
Speaker 2: the middle of the night because alcohol will help you

875
00:46:18,800 --> 00:46:20,960
Speaker 2: fall asleep at the beginning of the night to some extent,

876
00:46:21,040 --> 00:46:21,560
Speaker 2: but then.

877
00:46:22,320 --> 00:46:24,760
Speaker 1: You withdraw essentially exactly.

878
00:46:24,840 --> 00:46:26,520
Speaker 2: Withdrawal disrupts your sleep later.

879
00:46:26,760 --> 00:46:31,360
Speaker 1: Right, and and alcohol is constantly getting metabolized, and especially

880
00:46:31,719 --> 00:46:35,759
Speaker 1: if you are a frequent user of alcohol, then that

881
00:46:35,800 --> 00:46:38,120
Speaker 1: withdrawal that happens at two three am. I mean, we've

882
00:46:38,120 --> 00:46:40,600
Speaker 1: all been there, maybe not you, Emily, but or in

883
00:46:40,640 --> 00:46:42,040
Speaker 1: college if you remember, I.

884
00:46:42,000 --> 00:46:43,120
Speaker 2: Have been there, You've been there.

885
00:46:43,160 --> 00:46:44,960
Speaker 1: At the two to three am you wake up, you're

886
00:46:44,960 --> 00:46:47,279
Speaker 1: sort of you feel hyper, you feel uncomfortable's hard to

887
00:46:47,360 --> 00:46:50,440
Speaker 1: fall back asleep. Not great, not great, folks.

888
00:46:50,360 --> 00:46:52,759
Speaker 2: Okay, but let's talk about where alcohol is great, which

889
00:46:52,760 --> 00:46:54,719
Speaker 2: is making parties more fun. Right.

890
00:46:55,239 --> 00:46:59,880
Speaker 1: Basically, this is it's like everything in medicine is risk benefit,

891
00:47:00,080 --> 00:47:02,200
Speaker 1: and we've given you a lot of risks and we

892
00:47:02,280 --> 00:47:06,279
Speaker 1: don't have much benefits to share except that it is

893
00:47:06,400 --> 00:47:09,600
Speaker 1: fun and it makes social interactions, which is.

894
00:47:09,600 --> 00:47:11,520
Speaker 2: A real benefit. I mean, I think that is, like,

895
00:47:11,920 --> 00:47:16,040
Speaker 2: you know, it's an aspect of this conversation that's very frustrating. Again,

896
00:47:16,200 --> 00:47:18,760
Speaker 2: is this piece of it where it's like we're looking

897
00:47:19,239 --> 00:47:22,480
Speaker 2: for evidence that alcohol is like broccoli and it's not.

898
00:47:22,680 --> 00:47:25,200
Speaker 2: It's not like broccoli. Okay, Broccoli like you can have

899
00:47:25,239 --> 00:47:27,720
Speaker 2: as much of it as you want. It's super energy, dense,

900
00:47:27,840 --> 00:47:30,400
Speaker 2: a lot of fiber, Nature's brew. Broccoli is great for you,

901
00:47:30,400 --> 00:47:33,239
Speaker 2: a lot of vitamins. Whatever. Alcohol is not broccoli. But

902
00:47:33,800 --> 00:47:36,600
Speaker 2: we're talking about it like if it's not broccoli, it's

903
00:47:36,640 --> 00:47:39,640
Speaker 2: basically cocaine, and it's like at low levels, it's also

904
00:47:39,680 --> 00:47:41,319
Speaker 2: not cocaine. And I think we got to kind of

905
00:47:41,360 --> 00:47:43,640
Speaker 2: like find the middle here.

906
00:47:44,120 --> 00:47:45,440
Speaker 1: Yeah, what is is it? Bacon?

907
00:47:47,000 --> 00:47:49,880
Speaker 2: I think it's like dorito. It's like Dorito's or bacon

908
00:47:50,000 --> 00:47:53,239
Speaker 2: or something. It's like it's like fun, It's like it's

909
00:47:53,280 --> 00:47:55,239
Speaker 2: sometimes food, you know, all right.

910
00:47:56,280 --> 00:48:01,120
Speaker 1: I like that. The studies of alcohol and its social

911
00:48:01,120 --> 00:48:04,799
Speaker 1: effects are really fascinating. And whenever I read these, I

912
00:48:05,040 --> 00:48:07,279
Speaker 1: get sad. The eye study kidney disease, and I don't

913
00:48:07,280 --> 00:48:10,080
Speaker 1: actually get to run these trials. So these are studies.

914
00:48:10,239 --> 00:48:12,759
Speaker 1: These alcohol researchers bring people into the lab. Some of

915
00:48:12,800 --> 00:48:15,160
Speaker 1: them have their lab set up like a bar to

916
00:48:15,320 --> 00:48:18,399
Speaker 1: like have the full experience, so they have a bar

917
00:48:18,520 --> 00:48:20,799
Speaker 1: in their lab and then they're measuring out all these

918
00:48:20,840 --> 00:48:25,160
Speaker 1: precise amounts of alcohol. There's a fun study in psychological

919
00:48:25,200 --> 00:48:27,840
Speaker 1: science in twenty twelve. They took seven hundred and twenty

920
00:48:27,840 --> 00:48:32,279
Speaker 1: people strangers in groups of three, and they put them

921
00:48:32,280 --> 00:48:34,680
Speaker 1: in a room and they randomize them to drink alcohol

922
00:48:34,760 --> 00:48:37,200
Speaker 1: versus placebo, and then they saw, you know, how they

923
00:48:37,280 --> 00:48:39,680
Speaker 1: felt they just interacted and how they felt about each other.

924
00:48:39,800 --> 00:48:44,080
Speaker 1: And those who got the alcohol reported as significantly enhanced

925
00:48:44,200 --> 00:48:47,239
Speaker 1: positive affect, So good feelings about the other people are

926
00:48:47,280 --> 00:48:52,560
Speaker 1: reduced negative affect and increased self reported bonding. In short,

927
00:48:52,960 --> 00:48:54,960
Speaker 1: they had a good time and they made some friends.

928
00:48:55,200 --> 00:48:58,000
Speaker 2: Yeah, which is really that's what that's what's for, man,

929
00:48:58,080 --> 00:49:00,439
Speaker 2: that's what it's for. I don't know. I think that's

930
00:49:00,480 --> 00:49:04,040
Speaker 2: what I'm curious here. What is the placebo like? Does

931
00:49:04,080 --> 00:49:05,239
Speaker 2: it taste like alcohol?

932
00:49:05,719 --> 00:49:09,960
Speaker 1: Yeah? They do. They have stuff that tastes alcohol ish,

933
00:49:10,040 --> 00:49:12,919
Speaker 1: like medicinal I think maybe quinine or things. In some

934
00:49:13,000 --> 00:49:15,760
Speaker 1: studies now they are using alcohol free beer and alcohol

935
00:49:15,880 --> 00:49:19,439
Speaker 1: alcohol free wine de alcoholized wine, which now doesn't taste

936
00:49:19,440 --> 00:49:22,000
Speaker 1: as crappy as it did ten years ago. A lot

937
00:49:22,040 --> 00:49:24,359
Speaker 1: of advances I think. I don't know. Athletic I think

938
00:49:24,440 --> 00:49:25,040
Speaker 1: is pretty.

939
00:49:24,760 --> 00:49:27,120
Speaker 2: Good, but no, the beer is great, the alcohol free

940
00:49:27,120 --> 00:49:29,200
Speaker 2: wine is I still haven't made any progress on that.

941
00:49:29,719 --> 00:49:32,560
Speaker 1: Yeah. So yeah. And there are some other studies as

942
00:49:32,600 --> 00:49:37,000
Speaker 1: well that show, for example, in social interactions with alcohol

943
00:49:37,120 --> 00:49:43,000
Speaker 1: compared to without alcohol, alcohol consumption moderated seven of ten

944
00:49:43,040 --> 00:49:48,280
Speaker 1: associations between state social anxiety and indicators of healthy social interactions.

945
00:49:48,960 --> 00:49:52,280
Speaker 1: What this means is that you measure someone's general social anxiety,

946
00:49:52,360 --> 00:49:55,120
Speaker 1: how nervous they are, and how well they interact with

947
00:49:55,200 --> 00:49:58,440
Speaker 1: new people, and across seven of ten of those metrics,

948
00:49:58,600 --> 00:50:03,440
Speaker 1: alcohol improved the social interaction. So you don't want to

949
00:50:03,480 --> 00:50:06,960
Speaker 1: go too crazy when you're meeting new people like you know,

950
00:50:07,600 --> 00:50:12,080
Speaker 1: but it smooth things over. It does, it.

951
00:50:11,960 --> 00:50:15,120
Speaker 2: Does, and I think that's that's worth Uh, that's worth noting.

952
00:50:15,160 --> 00:50:18,439
Speaker 2: I mean, interacting with other people who you haven't met

953
00:50:18,440 --> 00:50:22,200
Speaker 2: before is one of my nightmares, and so I actually

954
00:50:22,280 --> 00:50:23,440
Speaker 2: really appreciate this.

955
00:50:24,239 --> 00:50:26,040
Speaker 1: I'm trying to imagine how you even do it. I

956
00:50:26,280 --> 00:50:28,279
Speaker 1: assume you just say hi, I'm Emily. Here is my

957
00:50:28,560 --> 00:50:30,719
Speaker 1: Whoop score for recovery score for today.

958
00:50:31,280 --> 00:50:34,919
Speaker 2: I don't like it. I don't I'm for someone whose

959
00:50:34,960 --> 00:50:37,279
Speaker 2: life is spent so aggressively in the public eye. I

960
00:50:37,320 --> 00:50:41,560
Speaker 2: am surprisingly shy and weird at parties, so.

961
00:50:42,160 --> 00:50:48,520
Speaker 1: I'm not entirely surprised that the nicest way you've a

962
00:50:48,520 --> 00:50:49,040
Speaker 1: long time.

963
00:50:49,239 --> 00:50:50,799
Speaker 2: I'm sure you were in college parties with me when

964
00:50:50,800 --> 00:50:52,000
Speaker 2: I was probably also super weird.

965
00:50:53,600 --> 00:50:55,200
Speaker 1: H No, you're always I was drinking.

966
00:50:55,200 --> 00:50:55,880
Speaker 2: Okay, let's move on.

967
00:50:55,920 --> 00:50:59,279
Speaker 1: Always wonderful and charming. I threw in a study for

968
00:50:59,320 --> 00:51:02,960
Speaker 1: you about I always have to find something about endurance athletes.

969
00:51:03,000 --> 00:51:06,120
Speaker 1: So do you buy the idea? As as told in

970
00:51:06,200 --> 00:51:09,600
Speaker 1: Sports Medicine twenty twenty six, a journal, we like that

971
00:51:10,160 --> 00:51:15,839
Speaker 1: the observational links between alcohol and death overall mortality were

972
00:51:16,000 --> 00:51:19,359
Speaker 1: restricted to only the twenty percent of people who got

973
00:51:19,360 --> 00:51:21,640
Speaker 1: the least amount of exercise. So if you're in the

974
00:51:21,640 --> 00:51:26,400
Speaker 1: top eighty percent of exercisers, the alcohol effect goes away.

975
00:51:26,800 --> 00:51:27,239
Speaker 1: Is that nice?

976
00:51:27,280 --> 00:51:30,680
Speaker 2: No, don't don't. I don't believe in this because it

977
00:51:30,719 --> 00:51:35,600
Speaker 2: is the set of people who are exercising are very

978
00:51:35,600 --> 00:51:37,279
Speaker 2: different from the set of people who are not. In

979
00:51:37,320 --> 00:51:40,360
Speaker 2: the people who are exercising wealth alcohol, like those differences

980
00:51:40,440 --> 00:51:44,200
Speaker 2: are smaller. This just feels to me like it is

981
00:51:44,719 --> 00:51:50,759
Speaker 2: entirely driven by different kinds of complicated biases. And so

982
00:51:50,800 --> 00:51:54,840
Speaker 2: it's good to exercise, but it doesn't mean that you

983
00:51:54,880 --> 00:51:58,759
Speaker 2: can just you shouldn't drink while you exercise. Don't put

984
00:51:58,800 --> 00:52:01,719
Speaker 2: fight in you're in your water bottle, my friends, it's

985
00:52:01,719 --> 00:52:02,160
Speaker 2: not good.

986
00:52:03,000 --> 00:52:05,120
Speaker 1: At the same birthday party, I was telling you about

987
00:52:05,239 --> 00:52:08,279
Speaker 1: that where the taco bell incident happened back in the

988
00:52:08,360 --> 00:52:10,719
Speaker 1: news portion of the show. What we did is one

989
00:52:10,760 --> 00:52:15,640
Speaker 1: of these group bikes where there's twelve people on a bike.

990
00:52:15,640 --> 00:52:18,000
Speaker 1: I don't know if you have one ountain Rhode Island,

991
00:52:18,080 --> 00:52:20,680
Speaker 1: and you bike around the city from bar to bar.

992
00:52:20,760 --> 00:52:22,960
Speaker 1: But it's really hard, like you get a real workout.

993
00:52:23,000 --> 00:52:25,960
Speaker 1: I was sweating and I loved it. I was drinking.

994
00:52:26,120 --> 00:52:28,680
Speaker 1: I was exercising and I was like, this is a

995
00:52:28,680 --> 00:52:31,759
Speaker 1: great combo. I feel good in so many ways.

996
00:52:32,000 --> 00:52:34,520
Speaker 2: I feel like you're on the bike with twelve other

997
00:52:34,600 --> 00:52:35,520
Speaker 2: people on the scene.

998
00:52:35,600 --> 00:52:38,879
Speaker 1: Yeah, it's a big oval of pedals, and so there's

999
00:52:38,880 --> 00:52:42,319
Speaker 1: a driver and everyone's pedaling together and the driver is

1000
00:52:42,320 --> 00:52:46,120
Speaker 1: steering it around the streets of New Haven. It's fun,

1001
00:52:46,520 --> 00:52:49,000
Speaker 1: but it's a work. I think you're at a hill

1002
00:52:49,000 --> 00:52:50,719
Speaker 1: and everyone's like, oh no, it's a hill, and then

1003
00:52:50,800 --> 00:52:54,400
Speaker 1: at the red lights he makes you drink. Wow.

1004
00:52:55,080 --> 00:52:57,360
Speaker 2: I mean, I grew up in New Haven and I

1005
00:52:57,360 --> 00:53:00,600
Speaker 2: never knew about this amazing bike. Okay, option, I'm totally

1006
00:53:00,600 --> 00:53:01,040
Speaker 2: doing that.

1007
00:53:02,200 --> 00:53:04,319
Speaker 1: All right. People are going to ask us, and so

1008
00:53:04,719 --> 00:53:07,160
Speaker 1: I'm going to ask you, Emily, who doesn't drink, and

1009
00:53:07,200 --> 00:53:09,800
Speaker 1: you can ask me who does. How much is okay

1010
00:53:09,800 --> 00:53:10,240
Speaker 1: to drink?

1011
00:53:12,800 --> 00:53:17,680
Speaker 2: I think, in my view, is in moderation, which is

1012
00:53:17,680 --> 00:53:23,239
Speaker 2: defined by yourself. So I would generally tell people I

1013
00:53:23,280 --> 00:53:26,400
Speaker 2: wouldn't have more than two drinks a day, because I

1014
00:53:26,400 --> 00:53:29,560
Speaker 2: think as you get up above there, you start seeing

1015
00:53:29,600 --> 00:53:32,799
Speaker 2: some of these impacts on more meaningful impacts on things

1016
00:53:32,840 --> 00:53:37,440
Speaker 2: like sleep. But I certainly would not tell people not

1017
00:53:37,520 --> 00:53:39,080
Speaker 2: to drink at all. I don't think that the data

1018
00:53:39,320 --> 00:53:39,800
Speaker 2: supports that.

1019
00:53:40,760 --> 00:53:44,359
Speaker 1: I agree. My party line is I tell people they

1020
00:53:44,360 --> 00:53:48,520
Speaker 1: can drink just enough, and it's their job to figure

1021
00:53:48,520 --> 00:53:50,879
Speaker 1: out what just enough is and to try to draw

1022
00:53:50,920 --> 00:53:53,399
Speaker 1: the line there. This is also something you can experiment with.

1023
00:53:53,560 --> 00:53:55,799
Speaker 1: I told you on a prior episode that I did

1024
00:53:55,840 --> 00:53:58,200
Speaker 1: dry January this year, so I didn't drink for a month,

1025
00:53:58,239 --> 00:54:00,600
Speaker 1: and I was waiting to feel as a maaz as

1026
00:54:00,800 --> 00:54:02,640
Speaker 1: everyone says you're going to feel, and it's like, oh,

1027
00:54:02,680 --> 00:54:04,920
Speaker 1: my sleep is so good and my energy and my focus,

1028
00:54:04,960 --> 00:54:08,040
Speaker 1: and I really just didn't feel any different. And that

1029
00:54:08,120 --> 00:54:09,640
Speaker 1: was a valuable experiment for me.

1030
00:54:09,800 --> 00:54:11,400
Speaker 2: But you can do that, and I will say I

1031
00:54:12,239 --> 00:54:15,360
Speaker 2: also experimented with this, had more or less the opposite experience,

1032
00:54:15,400 --> 00:54:19,760
Speaker 2: which is totally changes how I feel in the morning.

1033
00:54:19,840 --> 00:54:23,319
Speaker 2: And I think that's that's why self experimentation is helpful.

1034
00:54:25,000 --> 00:54:28,279
Speaker 1: All right, Emily, smash or pass alcohol?

1035
00:54:29,600 --> 00:54:30,920
Speaker 2: Smash in moderation.

1036
00:54:32,200 --> 00:54:37,400
Speaker 1: Yep, I am a smash. You know, alcohol is proof

1037
00:54:37,440 --> 00:54:41,160
Speaker 1: that God loves us. I believe it. So that's it.

1038
00:54:41,520 --> 00:54:43,719
Speaker 1: That's it for alcohol. You're mail bag question of the

1039
00:54:43,760 --> 00:54:45,160
Speaker 1: Week after the break.

1040
00:54:50,560 --> 00:54:54,319
Speaker 3: Hi, Emily and Perry. This is Kristen in Minneapolis. My

1041
00:54:54,440 --> 00:54:59,120
Speaker 3: question for you is about sleep apnea. Perry. You mentioned

1042
00:54:59,160 --> 00:55:02,239
Speaker 3: you had it in an earlier episode, and I was

1043
00:55:02,239 --> 00:55:04,640
Speaker 3: wondering if you would talk a little bit more about

1044
00:55:04,680 --> 00:55:09,600
Speaker 3: how to recognize it in someone someone who snores, like

1045
00:55:10,760 --> 00:55:12,360
Speaker 3: say my husband.

1046
00:55:13,280 --> 00:55:16,319
Speaker 2: Thanks so much, Okay, Perry, I'm going to pass this

1047
00:55:16,360 --> 00:55:18,360
Speaker 2: one to you as someone who has been through these

1048
00:55:18,560 --> 00:55:19,480
Speaker 2: this testing.

1049
00:55:21,000 --> 00:55:24,040
Speaker 1: Yeah, so for those of you who haven't gone through

1050
00:55:24,040 --> 00:55:28,040
Speaker 1: all the episodes. I have sleep apnia, use a seapap

1051
00:55:28,320 --> 00:55:31,080
Speaker 1: and that is something an intervention actually that has made

1052
00:55:31,400 --> 00:55:32,879
Speaker 1: I think my life a little better. I do think

1053
00:55:32,880 --> 00:55:35,640
Speaker 1: I have more energy using seapap. But more importantly, my

1054
00:55:36,360 --> 00:55:38,680
Speaker 1: wife still sleeps in the same bed with me, which

1055
00:55:38,719 --> 00:55:43,400
Speaker 1: is nice because snoring is the primary symptom of obstructive

1056
00:55:43,400 --> 00:55:46,839
Speaker 1: sleep apnia. Virtually everyone with obstructive sleep apnia is going

1057
00:55:46,880 --> 00:55:51,400
Speaker 1: to snore. Not everyone who snores has obstructive sleep apnia.

1058
00:55:51,440 --> 00:55:55,120
Speaker 1: That's how to think about this. The diagnosis of OSA

1059
00:55:55,680 --> 00:56:00,440
Speaker 1: requires a sleep study, that's the standard. This can be

1060
00:56:00,560 --> 00:56:03,040
Speaker 1: done in a sleep clinic where you actually go somewhere

1061
00:56:03,080 --> 00:56:04,520
Speaker 1: and you're in a bed and they have all sorts

1062
00:56:04,560 --> 00:56:07,160
Speaker 1: of monitors on you. But actually they've gotten really good

1063
00:56:07,200 --> 00:56:11,400
Speaker 1: now about doing this in your own home using portable devices,

1064
00:56:11,440 --> 00:56:14,800
Speaker 1: and what they're measuring for obstructive sleep apneas not snoring,

1065
00:56:14,880 --> 00:56:18,480
Speaker 1: but what are called apneas, so the cessation of breathing

1066
00:56:19,080 --> 00:56:23,960
Speaker 1: and hypopnias, which are periods of low levels of breathing.

1067
00:56:24,000 --> 00:56:26,000
Speaker 1: So they're measuring how your chest is moving up and

1068
00:56:26,040 --> 00:56:28,239
Speaker 1: down and their criteria for how many of those and

1069
00:56:28,280 --> 00:56:30,640
Speaker 1: how long they last to make the diagnosis.

1070
00:56:31,400 --> 00:56:33,680
Speaker 2: Is there a way to tell if you are just

1071
00:56:33,760 --> 00:56:37,680
Speaker 2: a person who's a very loud snorer whether you are

1072
00:56:38,480 --> 00:56:41,840
Speaker 2: not breathing like based on the noise? Is there a

1073
00:56:41,880 --> 00:56:43,200
Speaker 2: noise diagnostic?

1074
00:56:43,840 --> 00:56:51,160
Speaker 1: I mean spouses, bed partners can usually tell the person

1075
00:56:51,160 --> 00:56:53,920
Speaker 1: who is awake listening to you snore, will hear periods

1076
00:56:53,960 --> 00:56:56,960
Speaker 1: of apnea and or can hear periods of apnea and

1077
00:56:57,000 --> 00:56:59,359
Speaker 1: get nervous about it. You would think that someone would

1078
00:56:59,360 --> 00:57:01,399
Speaker 1: invent an app if that records you're snoring or something

1079
00:57:01,440 --> 00:57:03,560
Speaker 1: it gives you you would a risk store and maybe

1080
00:57:03,600 --> 00:57:06,359
Speaker 1: one exists, but none, certainly none are FDA approved for

1081
00:57:06,640 --> 00:57:11,799
Speaker 1: diagnostic diagnostic purposes. Tamor, our producer, just came up with

1082
00:57:11,880 --> 00:57:14,759
Speaker 1: the idea that it should be called app nia, which

1083
00:57:14,800 --> 00:57:19,439
Speaker 1: is genius patent pending wellness. Actually that's ours now. It'll

1084
00:57:19,480 --> 00:57:21,440
Speaker 1: be on the app store next week.

1085
00:57:22,120 --> 00:57:24,840
Speaker 2: My last question on this, and I'm just going to

1086
00:57:24,880 --> 00:57:28,800
Speaker 2: say this is just a personal question, is what's the

1087
00:57:29,560 --> 00:57:32,680
Speaker 2: let's say your partner is allowed snore but doesn't have

1088
00:57:32,800 --> 00:57:37,040
Speaker 2: sleep apnea. What are some ways other than kicking to

1089
00:57:37,200 --> 00:57:38,920
Speaker 2: get them to stop snoring?

1090
00:57:39,200 --> 00:57:39,720
Speaker 1: Yeah?

1091
00:57:39,840 --> 00:57:40,240
Speaker 2: Do you know?

1092
00:57:40,560 --> 00:57:45,560
Speaker 1: I do? Yeah? So yes, Waking them up not ideal

1093
00:57:46,400 --> 00:57:50,480
Speaker 1: because it'll come right back. Sleep position is the main one.

1094
00:57:50,560 --> 00:57:53,120
Speaker 1: So snoring happens usually for most people and they're sleeping

1095
00:57:53,120 --> 00:57:57,040
Speaker 1: on their back. People will tend to roll back onto

1096
00:57:57,040 --> 00:57:58,960
Speaker 1: their back once they fall asleep, if that's their comfortable

1097
00:57:59,000 --> 00:58:02,280
Speaker 1: sleep position. I have had some patients who have sown

1098
00:58:02,400 --> 00:58:05,360
Speaker 1: a tennis ball into a pajama shirt so that if

1099
00:58:05,360 --> 00:58:07,840
Speaker 1: they lie on their back, it sort of digs into

1100
00:58:07,880 --> 00:58:10,080
Speaker 1: them princess in the pea style, So they have to

1101
00:58:10,080 --> 00:58:12,919
Speaker 1: turn over to your sort of retraining. There are oral

1102
00:58:12,960 --> 00:58:17,000
Speaker 1: appliances that advance the jaw a little bit. I actually

1103
00:58:17,080 --> 00:58:19,360
Speaker 1: used one of these before I got my CPAP machine,

1104
00:58:19,720 --> 00:58:22,640
Speaker 1: and that opens up the oral pharynx so that snoring

1105
00:58:22,680 --> 00:58:25,320
Speaker 1: doesn't happen. They can be kind of uncomfortable. It's uncomfortable

1106
00:58:25,320 --> 00:58:28,960
Speaker 1: for me, but their dentists who specialize in this. There

1107
00:58:29,000 --> 00:58:33,960
Speaker 1: are surgeries that can eliminate snoring, soft palette surgeries, and

1108
00:58:34,000 --> 00:58:38,760
Speaker 1: then for people with an elevated BMI weight losses, a

1109
00:58:38,880 --> 00:58:40,360
Speaker 1: primary method to reduce storing.

1110
00:58:40,840 --> 00:58:42,439
Speaker 2: So I'm hearing kicking.

1111
00:58:43,080 --> 00:58:44,720
Speaker 1: Or tennis ball in the back of the.

1112
00:58:44,680 --> 00:58:45,640
Speaker 2: Shkicking or tennis ball.

1113
00:58:45,800 --> 00:58:47,480
Speaker 1: Okay, both are fun.

1114
00:58:50,520 --> 00:58:53,040
Speaker 2: All right? That is it for us today. Stick with

1115
00:58:53,120 --> 00:58:56,200
Speaker 2: us next week when we'll ask what's the deal with

1116
00:58:56,320 --> 00:58:56,960
Speaker 2: the sun.

1117
00:59:00,040 --> 00:59:00,120
Speaker 4: On?

1118
00:59:00,160 --> 00:59:00,800
Speaker 1: What the hell is?

1119
00:59:01,480 --> 00:59:04,480
Speaker 2: We'll get into it anyway.

1120
00:59:04,720 --> 00:59:06,280
Speaker 1: Is it? Is it a god?

1121
00:59:07,480 --> 00:59:11,480
Speaker 2: Come back? Next week you'll find out. Wellness Actually is

1122
00:59:11,560 --> 00:59:16,280
Speaker 2: produced in association with iHeartMedia. Our senior producer is Tamar Avishaik.

1123
00:59:16,680 --> 00:59:20,320
Speaker 2: Our executive producer at iHeart is Jennifer Bassett. Our theme

1124
00:59:20,360 --> 00:59:23,000
Speaker 2: music is by Eric Deutsch, and our content is for

1125
00:59:23,120 --> 00:59:24,360
Speaker 2: educational purposes only.

1126
00:59:25,040 --> 00:59:27,320
Speaker 1: If you like the show, help other people find us,

1127
00:59:27,680 --> 00:59:30,439
Speaker 1: leave a rating and review on Apple Podcasts or your

1128
00:59:30,520 --> 00:59:33,240
Speaker 1: podcatcher of choice, and help us spread the word about

1129
00:59:33,280 --> 00:59:36,320
Speaker 1: the show. You can follow us on Instagram at Wellness

1130
00:59:36,360 --> 00:59:39,560
Speaker 1: Actually pod and don't forget We want to hear from you.

1131
00:59:39,960 --> 00:59:42,480
Speaker 1: Head over to Wellness Actually dot fm and leave us

1132
00:59:42,480 --> 00:59:45,080
Speaker 1: a question for our mailbag, or suggest a topic for

1133
00:59:45,120 --> 00:59:45,760
Speaker 1: a future show.

1134
00:59:46,560 --> 00:59:49,200
Speaker 2: We'll let the influencers have the last word. I don't

1135
00:59:49,200 --> 00:59:53,360
Speaker 2: want to alarm anyone, but I think there's a hockey

1136
00:59:53,440 --> 00:59:56,080
Speaker 2: call in this story. Do we have a word, sense

1137
00:59:56,160 --> 00:59:59,880
Speaker 2: of well being and any Sorry my speech, You're right,

1138
01:00:00,160 --> 01:00:10,400
Speaker 2: give me noth