The Dr. Hyman Show - Understanding Nutrition Research

Episode Date: December 20, 2019

We’re always hearing extreme results from nutritional research. Ranging from the “dangers” of coconut oil and eggs to completely contradictory statements, like that a low-carb diet shortens lifespan and also increases it, we are bombarded with confusing information that can make deciding what to eat totally overwhelming. But with the right knowledge and resources, we can learn to decipher dietary studies to get the real truth. In this mini-episode, Dr. Hyman sits down with Chris Kresser, an expert in Functional Medicine and a leader in nutritional research, to dissect what you need to evaluate to understand nutrition research. Chris Kresser M.S., L.Ac is the co-director of the California Center for Functional Medicine, founder of Kresser Institute, creator of ChrisKresser.com, and the New York Times bestselling author of The Paleo Cure and Unconventional Medicine. He is one of the most respected clinicians and educators in the fields of Functional Medicine and ancestral health and has trained over 1,500 clinicians and health coaches in his unique approach. Find Dr. Hyman’s full-length conversation with Chris Kresser at: https://DrMarkHyman.lnk.to/ChrisKresser

Coming up on this mini episode of The Doctor's Pharmacy. For the majority of people, 67% of people in these studies reported a calorie intake that was so low that not even an elderly, frail, bedridden woman could survive on it. When it comes to looking at studies on nutrition, we need to ask the right questions. What type of study is it? Who funded it? What kinds of variables were and weren't accounted for? In this mini episode, Dr. Hyman sits down with functional medicine clinician and educator Chris Kresser to help unpack what we need to consider when it comes to nutritional research. When you hear a headline like this, and I'm sure people are going to have heard this headline

in the news that meat kills, it doesn't actually represent what the science says. So the consumer's confused, the media is confused, doctors get confused, and everybody's confused, but it's not so confusing. And you shed light on why it's not so confusing. So let's start with, at first, why we have problems with these studies and why we're so confused. And then we'll get into the real data on meat. Okay. Yeah. So, I mean, as you pointed out, this study, along with many others like it, I could set my watch by how often these studies come out claiming to show that meat increases the risk of death. We see it all the time. My email box blows up, social media blows up. But you said.

Yeah, you said. And now at this point, I'm just linking to some articles I've written that has everything there and not even bothering to write anymore, because it's the same response every single time. And the response is, as you pointed out, when you do a large observational study, you're just showing two factors that are occurring together. You're not demonstrating a causal relationship that one factor is causing another factor.

And there are many different examples of this. There's one blog name. I think the guy's name is Tyler. I can't remember his last name, but it's called Spurious Correlations. Yes. And he has basically collected a bunch of correlations that have nothing, clearly nothing to do with one another.

Like the margarine consumption is like 99.7 correlated with the divorce rate in maine maybe it's connected because you know trans fats i would get a divorce probably if my wife was feeding me margarine honestly my mother used to give me fleischmann's margarine when i was a kid because that was the 70s and tang and fleischmann's margarine were the future foods right right so. So it's really tempting to assume that variables are causally related when they're not. And it's not a safe assumption, especially with the case of red meat, because of something called the healthy user bias or the unhealthy user bias, depending on how you look at it. So for decades, we've been told that red meat

is not a healthy food. And so people who on average in studies, if you're looking at the general population, people who tend to eat more red meat also tend to engage in more behaviors that are perceived as unhealthy. So they might smoke more, they might drink more, they eat fewer fruits and vegetables. They're not as physically active, they're not as well educated, they're lower income, which has nothing to do with their value as a person, but these are correlated with higher risk of death more strongly than any nutritional factor. And their microbiome is probably not as healthy because they eat lots of processed and refined food. And researchers try to control for some of these confounding factors, but there's no way that they can ever control for all of them.

Another huge problem with nutritional epidemiology is the way that data are collected. So most people are shocked when they learn just how ridiculous this is. But they use these assessments called food frequency questionnaires or other what are known as memory-based assessments or memory-based measures. Like what did you have for lunch last Thursday? What did you have for lunch last Thursday? How many servings of red meat did you eat four weeks ago, Mark? I mean, you and I think about food probably more than- 17.65 servings.

And you and I think about food more than probably just about anybody, you know? I can tell you what I ate yesterday, but that's about it. Right, right. And to illustrate this, some researchers did an analysis of the nurse's health data, which is what these new studies were based on. The nurse's health study, the meat kills. And it's not like it was a 10% increase in risk. We'll get into that. So they did an analysis of these memory-based assessments,

and they found that for the majority of people, 67% of people in these studies reported a calorie intake that was so low that not even an elderly, frail, bedridden woman could survive on it. That's right. So these are obese, overweight people that are reporting a starvation level calorie intake. So that alone just throws out the validity of all of the rest of the data because it would skew protein intake, fat intake, carbohydrate intake, and intake of every other food and nutrient.

In other words, bad memories of their life. Yeah. I mean, I remember when I was in training we were taught okay whatever people tell you they eat double it and whatever people tell you they exercise cut in half that's right people always report they want to please you they want to please you or they want to do like if they want to report what they think you want to hear essentially that's unless you're like weighing and measuring every single meal every day and writing it down word then you know you're not gonna be able to report what you ate i mean i don't know how much i ate no whatever i'm like i ate a little this had some of that i

took a few bites of this like and if you even if you if you had two plates and the only difference you know one had 100 or 200 more calories visually you couldn't even tell the difference so like you said unless you're weighing and actually specifically measuring them. And even then there are challenges on how to do that best, you know, in a ward. And Mayo Clinic wrote a big review showing how these types of assessments, these memory-based assessments or food frequency questionnaires really weren't valid. And they were so undermining the quality of all the science they're based on. So almost everything we hear about nutrition, almost everything is based on these type of

studies, which are fundamentally flawed. And there's a guy named John Ioannidis, you know, is a Stanford professor who loves to study studies. And he's like, 80% of these type of studies become observational or population studies or comparison studies. 80% get proven wrong ultimately when they're subjected to randomized controlled trials, which is a true experiment, right? Yeah. I mean, he's extremely critical of nutritional epidemiology. He's basically said that it's worthless in the way that it's constructed now. And it could be improved by better measurement techniques and using some new technologies to do that more effectively.

Maybe like pictures of their food and then it goes into a computer AI system and it measures them. That would be cool. And then they're testing that and that would help a lot. The other problem you also mentioned is in any field outside of nutritional epidemiology, an increase of risk of just 10% would be seen as completely insignificant meaningless that you have to noise at least a doubling of risk like a hundred percent increase or a twofold increase or more in order to be able to know that you're not just dealing with chance you know that indistinguishable from chance and the guy who published the the study, Walter Willard, and others at Harvard, and this comes

out of Harvard. These are stand-up guys, but they've spent their whole life committed to epidemiology. And they defend it tooth and nail. I remember speaking to Ron Krause, who's an experimental scientist who studies cholesterol and heart disease. And he's like, listen, listen you know those are helpful for generating hypothesis but they don't prove anything and these guys are running around saying that they're proving something are misleading people and and he said you know you need to see a change of at least

you know two and walter willard said to me once you know well we found this with smoking that smoking causes cancer, but the increased risk was 20 or 2000. Two to 3000%. Not 10%. Yeah. So like if it's 10% and not a thousand or 2000%, at least a hundred percent. Yeah. It's,

it's, it's, it's just worth ignoring. Like the thing with eggs that came out recently, which were, Oh, eggs are terrible, but it's like...

13%, 8%. I mean, rarely in nutritional epidemiology do you see any effects, even over 50%, much less 100%. And usually it's more in like the 10% to 15% range, which is meaningless. It's crazy. And you can also see on both sides of the aisle, you'll see studies that are epidemiology that show that meat's completely safe in the same way. And they're both kind of meaningless. Yeah.

I mean, so, you know, to be fair, it's really hard to do large randomized controlled trials in nutrition because you got to, like you said, you have to lock people in a metabolic ward and keep them there for 20 years because of the effects, especially if we're looking at real outcomes, which rather than just endpoints like cholesterol. There is something called the Bradford Hill criteria, which are criteria that actually I think were created around the time that these smoking studies were done because they're like, look,

we can't just do a randomized control trial with smoking. We can't wait around for, yeah. Okay. You guys smoke for 20 years. You guys don't smoke. We'll see what happens. So we need to figure out ways to better determine whether these correlations are actually meaningful and that there might be a causal relationship. And one of those ways is what is the effect size? How much of an increase do you see? Another is, do you see, is it monotonic? Like, does it continually grow, go up with an increased dose? Is it a linear relationship? Yeah. Is there a dose effect response? And there are many other criteria that you can use to kind of get closer to the idea that there's a causal relationship,

but those are rarely applied in these kinds of studies. We are bombarded with confusing information that can make deciding what to eat totally overwhelming. But with the right knowledge and resources, we can learn to decipher dietary studies to get to the real truth. Thanks for tuning in to this mini episode of The Doctors' Pharmacy.

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