Written by Christopher Kelly
Aug. 22, 2015
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Christopher: Hello and welcome to the Nourish Balance Thrive Podcast. My name is Christopher Kelly and today I'm joined by my good friend, research scientist, biochemist, Dr. Tommy Wood. Hi, Tommy.
Tommy: Hi, Chris.
Christopher: Hi. So we're here today to talk about a new paper that was published in the journal Cell Metabolism. It's an important paper. The title of the study is Calorie for Calorie: Dietary Fat Restriction Results in More Body Fat Loss than Carbohydrate Restriction in People with Obesity. That's quite a long title. Maybe why don't we start by describing the study, the design and what it was intended to show?
Tommy: Yes. I think by the time people listen to this podcast, they'll probably have read 20 different opinions and articles about this in the popular media and knowing of this podcast, various people in the low carb community and all that kind of stuff. Because not many people have actually have access to the paper, we can kind of walk through it and give our sort of various opinions on what happened.
Christopher: I should point out, actually it is an open access paper. I didn't realize it at first but it is open access and there is a PDF online. So I can link to that in the show notes so that people can read the paper. I think it's well worth their time.
Tommy: Absolutely. And it's not very dense in terms of the general language of the paper. So it's definitely worth reading, at least the first half. If you go into the supplementary material, there's like 30 pages of mathematical equations.
Christopher: I didn't read those, I'll be honest.
Tommy: Yeah. So you can skip that bit. But the rest is very interesting. And actually, probably lots of people, it's definitely worth reading.
Christopher: Okay. So calorie for calorie, diet fat restriction results in more body fat loss. This sounds like something I want. So you're telling me now that I should reduce my dietary fat and that's going to result in more body fat loss.
Tommy: Well, technically, yes. We'll talk about the design of the study first. They had 19 obese people, nine were female, ten were male and they did what we call a crossover study. So everybody got both diets. And what they did is, at random, they randomize which diet they got first. So they come in to this place called a metabolic ward and this is run by the NIH. So it's the NIH Metabolic Ward. And basically, you come, they spend a few days on a baseline diet. The baseline diet was 50% carbs. I think it was 50% carbohydrate, 35% fat and 15% protein.
Christopher: And it was not much different from what the patients had or the participants had already been eating in the recent past.
Tommy: Yes. It was a rough estimation of what they normally eat from day to day. And that was their run in diet. And then what happened was they restricted one macronutrient. So it was either carbohydrates or fat depending on which diet they were doing. And they restricted it so that they reduce calories by 30% and everything else was kept the same. So the only thing they did was restrict the number of calories from carbohydrate or the number of calories from fat.
So if they restricted calories, then the amount of fat and amount of protein they're eating stayed the same and they're restricting fat, vice versa essentially. And the reason they did this is because every other study that's been done out in the real world, if you're doing big randomized controlled trials of diets, then you basically tell a patient what to eat. Or some studies do actually give patients the actual food. But then they tend to be smaller. You basically just tell people what to eat. Or you give them guidance on what to eat. And you kind of just trust that they do that.
But then everybody says that a low carbohydrate diet offer a metabolic advantage, which basically means that either you burn more fat than you would otherwise think you do in terms of the calories you eat. Or it increases your metabolism more than cutting fat, low fat diet or low calorie diet. But then people say, there's all these other confounders. People around the real world that maybe eating other things. They may be doing more exercise. They may be sleeping more or whatever. So you can't control any of that.
So the reason they did this is that the patients come in, they do the same amount of exercise every day, they eat the same food every day and you can even -- They even keep them so that they can measure their metabolism overnight, their baseline metabolism during the day and they can measure their respiratory quotient, which is basically the ratio how much CO2 to oxygen they're breathing out, which basically tells you whether they're burning carbohydrate or fat.
[0:05:09]
That's the rough idea. So you can kind of measure all of this stuff. In terms of that, this is probably the most well-designed and most rigorously controlled study of diets that's ever been done. And this is purely to look at what happens if you in the short term restrict either carbohydrates or fat and everything else stays the same.
Christopher: Wow. That really is an important paper then, one that's well worth your time reading because this is quite unique.
Tommy: Yes. I mean, the problems come when you actually try to apply this to the real world and that's what we'll talk about more. But for the real like metabolic geeks, the short term changes they did and the things they measured, nobody has done that before. So it's so well controlled in many, many ways. They even made sure that the women were in the same stage of their period at both times during both diets because that can have an effect on metabolism.
Christopher: How much do you think this study cost?
Tommy: Millions. I'm sure it cost millions of dollars.
Christopher: Wow. Richard Feinman is just on the other line and he's going to join us. I should Skype him right now. Richard David Feinman, the middle name is important, is a professor of biochemistry at the State University of New York Downtown Medical Center, Brooklyn, New York. I'm really excited to have you on because you've obviously seen a lot of studies and the study that Tommy and I have just been talking about is the study in the Journal Cell Metabolism, Calorie for Calorie: Dietary Fat Restriction Results in More Body Fat Loss than Carbohydrate Restriction in People with Obesity.
Tommy already described the design of the study and what makes it groundbreaking. I would like to ask you what was your initial impression of the paper? Did you think it was one that was worth your attention? You've obviously seen a lot of studies over the years.
Richard: Well, it's worth my attention because I know Kevin Hall and he's a pretty smart guy and this is a pretty distressing paper. One of the reasons is that all of the data is reported as group averages. And the problem is that nobody loses an average amount of weight. The assumption of group averages is that there's a normal distribution or at least essentially limited distribution and the underlying idea behind that is that people are roughly the same and that minor variations account for the spread of the data. Those aren't good assumptions.
But they're okay if the data do not have big variation. Now, that's not true in this study. Now, one of the details here is the technical detail. Do you know the difference between the standard deviation and standard error of the mean?
Christopher: I am familiar with those but why don't you just describe? Can you describe briefly what the differences are?
Richard: Yeah. What a standard deviation is, think of the bell shaped curve. That's what we expect from a kind of random variation. We have this vision of dropping a series of solid balls and if everything were ideal, they'd all hit the ground at the same time. But due to wind variation or peanut butter left on one of balls, it's going to be random error in that measurement. Now, so the spread -- You can do statistics of the type that we're talking about here if you have what is called centrally limited data. In other words, the data kind of lumps around the center of the bell shaped curve.
So if you have that, then you want to know how far out are things? I mean, what is the variation if the average person lost five pounds, what happened to the rest of them? In another way, if the average person lost five pounds, then somebody must have lost four or three.
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So what's going on there? So what you do is you use the area -- You set off limits on your bell shaped curve. And so you imagine just drawing a line through two places and saying that's how far out you go to see how many people are there. What a good indicator of the spread is what's called the standard deviation. What that is, is it's part of the bell shaped curve. It contains about 69% of the data. And if the standard deviation is big, then it's a mess. If you have a standard deviation of four pounds when the average is five pounds, that means you don't know much because it means that some people lost one and some people lost three, some people lost ten. It's just a random very poor collection.
Now, the variation on this, the other feature that you need to know is if you're comparing two bell shaped curves, how do you know that they're really different? Because bell shaped curve is nice and smooth and it looks great but usually you don't have enough points. You have stuff that looks like a bell shaped curve but you may have only ten points. Let me interrupt here to say that I'm not a statistician. And one of the problems I have with the medical literature is that if I have to tell them how to do statistics, they're in a heap of trouble.
The standard error of the mean is what used to compare two bell shaped curves. Cutting to the chase, you may have to look at the standard error of the mean, which is what it says. It's kind of the statistics on the mean rather than the statistics on the raw data. So I'm sorry for this long winded stuff. I don't like statistics anymore than you do but the bottom line that you need to know is the standard error of the mean, which is what is reported in Hall's data, always make your data look much better than it really is. That's the bottom line.
Tommy: Absolutely. I'm sorry. Richard, I was going to say it's a real pleasure to have you or speak with you or have the opportunity to speak with you. And I've spoken on Chris' podcast before a little bit about this particularly to do with the research in sports medicine and where people use the mean plus the standard error of the mean just to make that data look a lot nicer than it really is. Whereas, what we should probably be doing, I don't know if you'd agree, but what maybe they should have done in Hall's paper is because you can't say that this data is normally distributed because the sample size is so small then you'd have to assume that it's not normally distributed.
And then you can't use the mean. You can't use an average. And instead you'd have to use a median, which is basically just the middle value of all of them. And then give something like an interview.
Richard: Yeah, but it's -- You can get around that. I mean, I don't mean you can get around it but if you take them at face value, that is assuming that it's a normal distribution, then all you have to do is convert the standard error of the mean which you reported to a standard deviation. Because you know how to interpret the standard deviation because it's 69% of the data will fall on that range. And the way you do that is you take, you multiply by the square root of the number of subject. And when you do that, you see that this big variation in his data.
The real question now, which is true of 80% of the medical literature, is why report a mean in standard deviation at all? Why not plot the points? Now, of course, if you have a random controlled trial of 53,000 subjects, it's not going to look that good. Although there are ways around that. But he's got 19 subjects. I want to see the data. I don't know that anything was done here.
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In other words, what do you want to know from a diet experiment? You don't want to know what the average was because it may not be average. You as a dieter or somebody who's reading about diets want to know should you bet on diet A or diet B? And so you don't want to know the average. What you want to know is how many people did better on diet A than on diet B and vice versa? And you can't tell that from his study. So we don't know whether he did anything here.
Christopher: That's quite a point. Basically, he summarized the data and to show the summary when it would have been more appropriate just to show all of the data. There wasn't that much data. He could have just given it all.
Richard: Only if he wanted to show what was going on.
Christopher: Okay.
Richard: And I don't mean him. The whole medical literature is full of this slavish attachment to statistics. And I have a golden rule of statistics. If you wait one second, I'll try to find it. And it comes from a book called QED Statistics, which is a pretty good Statistics book. And here's a very short quotation from the book QED Statistics. It says: The onus is on the author to convey to the reader an accurate impression of what the data look like using graphs or standard measures before beginning the statistical shenanigans. Any paper that doesn't do this should be viewed from the outset with considerable suspicion.
So I review Hall's paper with a lot of suspicion. There's something fundamentally wrong with the whole idea. Because what do you do in science? What you do is you try to make progress from previous studies. So if you see a study that seems to have a strong point but has some experimental flaw, then you try to redo the experiment and correct that flaw. For example, there was a mouse study showing that mice on low carb ketogenic diets did everything that we expected ketogenic diets. They have lost more weight. Their lipids were better, everything.
People were concerned though that it seemed to have a very low amount of protein. So we redid the experiment using a normal amount of protein or something more reasonable for a mouse and their data were all wrong. In fact, they didn't behave at all like people on a ketogenic diet. They got fat. They got fat even we took out all the carbohydrate. We made the deduction that mice may not be a good model for what happens to people in low-carb diet.
Anyway, not to get off the track, what is the state of low carbohydrate diet? Because what Hall used was a marginal low-carb diet. This paper is about low-carb diet. Because people are interested in low-carb diet. He knows it. You know it. What is the state of the art? Well, the state of the art in my view is Volek's paper with 40 people with metabolic syndrome and he put them on a low-carb diet and low fat diet. The low-carb diet did way better than the low fat diet on everything. It also had, you may recall this, there was more saturated fat in the blood of the low fat group even though they, the low carb group, has three times the amount of saturated fat in their diet. So this was a kind of landmark experiment.
You could say that depended on people recording what they ate. First of all, that's different than dietary recall as you do in one of these big epidemiologic studies. They wrote down stuff while they were eating it. I have some of their notebooks. It's good data. But you can say you write it down but your girlfriend comes into the room and write the wrong thing. It has a lot of error. So you redo what Volek did except you do it in a metabolic ward. Why didn't Hall do that? That's what you do in science.
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He went out and he did something else, doesn't explain why it's something else. I consider it hostile. It's dismissive of what is going on in low carb research.
Christopher: And then, Tommy, we've talked about this before. Even when you get people to write down the food as they're eating it, you still might see some bias. Do you think that the metabolic ward would have fixed the problem that Richard has described?
Richard: I don't think there was a problem.
Christopher: Okay.
Richard: I think metabolic ward is overkill in a study like that because the principle -- How do you deal with error? All experiments have error. Well controlled experiments in, I don't know, dissociation of organic acids have error. The question is whether the error is compatible with the conclusion that you wanted to draw. There's undoubtedly error in Volek's experiment. But half of the people in the low carb study that he did lost more weight than everybody in the low fat study. And some of them lost a large amount of weight.
In addition, it's not completely an error because the low carb people were in ketosis and the others weren't. So the principle is you have a big effect or you have a big effect here. And so, sure, it would be great if you can afford to do a metabolic ward study. But that would be appropriate. And Volek's is not the only low carb study that has very clear convincing data. If we do an experiment where you have a marginal low-carb diet and you have a small number of people for short term period of time, you can expect big numbers. I just don't know what's going on there. Like I say, it's because I know Kevin Hall for a while and he's a smart guy. I don't know why he did this.
Christopher: Okay. What am I to think then? I looked in the popular press, just by every newspaper that you can think of that covered this story, the conclusion went either way. Some people decided that the low-carb diet was best and another said that the low fat was best. And so what you're saying is you can't reach any conclusion at all with the paper as it's presented.
Richard: No. It didn't give you the data that you want because the differences are too small. I mean, one of the problems you have in the medical literature is this idea that if something, if two things are statistically different that, therefore, they are of consequence. And that's ridiculous. You know that. And they make it sound good by reporting rather the risk and presented the changes. I think somebody reported that there was -- Actually, the data that he had, there wasn't much difference between the groups on anything, which is what you expect. I mean, what happens in a week?
Christopher: Right, right, of course. And then so, you don't buy into this idea that you can just take these small changes and extrapolate them out over months or maybe years using the mathematical model that Hall developed?
Richard: No. I don't believe it. You don't believe it.
Christopher: No, I don't. I don't. The problem is then with the study design, the low carb group, it wasn't low carb enough.
Richard: Yeah. And the low fat group was too low fat. So you biased it. I mean, the low fat group, I think we go that low in low fat you get something. And the low carb group was absolutely marginal. I mean, I was going to say I'm in a maintenance phase with my weight. I'm not sure that's what I intended but that's certainly where I am. And I don't watch my diet that carefully because my main concern is obesity and I'm pretty stable now and so I don't pay much attention to it and I'm sort of a foodo anyway. If it tastes good, I usually eat it anyway.
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I rarely get 100 grams of carbohydrate in a day. Even the days when I have a Haagen-Dazs bar. They had 142 grams. How can that be? You're setting it up to get poor data.
Christopher: Yeah, I would agree with that, actually. I'm also eating a very low carbohydrate diet and measuring blood ketones. 140 grams, I would really have to go for it to eat that. I mean, you'd have to eat carbs with every meal, I think, which I know a lot of people listening to this podcast are probably not doing. They're saving their carbs to the evening. It is a really strange choice. And there was certainly, the fasting insulin in the two groups was what I would consider to be quite high. It didn't change at all with carbohydrate restriction which was quite surprising.
Richard: One of the things I'm concerned with is I consistently say you can't invoke who the sponsor is to compromise the work unless you know something for sure. So in other words, Coca Cola can sponsor perfectly good studies and if the experimenter says that Coca Cola had no input then you got to take him at his word. Otherwise, you got nothing. But this is intramural study. And the NIH is notoriously biased against low-carb diets. Certainly, the study wasn't compromised at all but the study design should not have been approved.
Christopher: Really?
Richard: It's an in house -- I don't know why they went with it? Not a good study. Like I say, like you say, it's designed to not give you real data.
Christopher: Why would this be then? Why would the NIH be biased against studies looking at low-carb diets?
Richard: Why would the NIH? I don't know. Because everybody has been biased against it for 40 years.
Christopher: Yeah. And then what do you make of--
Richard: But the NIH is not the government. The NIH is the scientific direction. They're not the same guys that are doing low fat studies and have been for 40 years.
Christopher: Okay.
Richard: The NIH is us. I always say, more widely quoted than the original is: We have met the enemy and he is us.
Christopher: And then what do you make of the reference to Gary Taubes more than once in the paper which it seems a bit strange to me. He's a very popular author and lots of people have heard of him and he's a physicist. But my understanding, he hasn't--
Richard: Gary is not a physicist. I don't know what he studied but Gary is not a physicist.
Christopher: Okay, sorry. I stand corrected on that. But also he's not published any other papers either, has he? It's kind of interesting that he should be--
Richard: Gary is not a scientist and most of the time he will admit that. Nobody has done more to bring out the science in low-carb diets than he has. He doesn't understand everything about it. Gary's main fault is that he thinks he doesn't have anything to learn from me. No. He has become a representative of low-carb diets and deserve at least so. I mean, he stood up to an establishment that's overbearing, which is different than saying everything that he writes about low-carb diets is correct.
I mean, a lot of the progress that we made has been done by non-scientists. [0:29:43] [Indiscernible] and Gary Taubes, their message really is you got to look at the science. You can't keep ignoring this stuff. The problem that you have is that this is not really a science at all.
[0:30:02]
At some point, it just went off the deep end in terms of not adhering to scientific studies. And what they've done, I say it as a joke that I'm the only biochemist dumb enough to stay in this field. But it's true. They've chased out everybody who has integrity. And our paper had 26 authors. This is our paper on diabetes, which we consider a kind of landmark paper. It's kind of a manifesto of low-carb diets for diabetes.
But we had trouble thinking of who could review the paper. When you submit a paper to the journal, peer review, you want to suggest people who could review the paper who at least could give it a fair shake. We had trouble thinking of anybody who could do that. You get 26 authors looking at low-carb diet seriously you may have exhausted the field. So it's a problem and we got to turn it around. The United States by analogy does not dig up Stalin's tomb. We don't blame people. But we do have to move on. And we do have to give credit to the people who've done it. I mean, it takes -- You know that Volek is a power lifter. But it also takes the mentality of a power lifter to stay in this field.
Christopher: I thought we were going to start talking about the differences in ghrelin and cortisol and some other markers on the study but you've kind of lampooned that by saying that none of the dietaries, is either missing or is not relevant as presented. Is there anything that you wanted to add to this, Tommy? I mean, you peer review science. I mean, what would you say?
Tommy: Well, I think the real issue is the fact that everybody has taken this study far outside of the context of what it could reasonably expected, be expected to answer. In terms of what it does that's new is the fact that it's the first study whereby they kept everything controlled including calories and all they did was change one macronutrient. The arguments against previous studies in the low-carb diets is that they haven't controlled the protein which is an argument that you can use just because you're trying to make a point when in reality that's not the point that you're trying to make. And that's not the point of those studies.
But they've made sure that didn't happen here. Before we started recording, we talked about the fact how they've stacked the deck here to make sure that the low-carb diet comes out below the low fat diet because of the way they did the run in diets. So that when they remove 30% of calories, it's a very, very low fat diet but only a moderately low-carb diet.
Christopher: I think it's worth -- Can you describe why is it that they did that? Because there was kind of some specific reasons why they couldn't make the low-carb diet any lower carb?
Tommy: Yes. So basically, what happened is they needed, to start with, they needed to give the participants the same number of calories as they would be expected to have to maintain weight, to maintain their weight and match their energy expenditure. And they did that using a dietary composition of what they were eating before roughly. These guys are free living people. They're probably just eating -- We'd expect them to be eating something close to a standard American diet, which was 50% carbs, 35% fat and 15% protein in terms of calories.
And then from there, they just want to reduce 30% of calories. But the problem was if they wanted to reduce carbs even further and actually make it a true low-carb diet, they'd have to increase fat to make the calories the same. So because of the diet they started within the baseline diet, they couldn't restrict calories. They couldn't restrict carbs even more. And they do actually admit to that fact. So what I was going to say is that you can almost defend their study design because of that. They were trying to keep everything the same and all they wanted to do was see what [0:34:39] [Indiscernible].
Richard: But why? Why were they trying to keep everything the same?
Tommy: Because previously -- So whenever I get into an argument with somebody about -- Low carbs diet are absolutely, particularly the people who are obese or insulin resistant or are type II diabetes, low-carb diets are by far the best compared to low fat or low calorie and we know that.
[0:35:00]
I mean, obviously, Richard is the world expert on this. But anybody who's trying to shoot down low-carb diets, they always have things like, "Well, they didn't control for protein." Or, "They didn't control for calories." Because what happens when you start a low carbohydrate diet is you automatically reduce your [0:35:18] [Indiscernible].
Richard: So you do a study where you don't control for a low-carb diet. It doesn't make any sense. What do you want to find out? You want to find out that the diet that we think is most effective for metabolic syndrome and diabetes and therefore most metabolic problems, that's the starting point.
Tommy: But it depends--
Richard: You define the study according to what you're testing.
Tommy: But it depends on the question that you're trying to--
Richard: The controls--
Tommy: It depends on the question you're trying to answer. Because surely, I'm not defending the study design, but all I'm saying is in order to answer the question they had, which was purely reducing 30% of calories by either carbohydrate or fat, what are the short term metabolic implications? And they do, I mean, apart from the fact that their statistical analysis is terrible, they do go some ways answering that. And it does not translate to the real world at all. But for the people who were questioning the studies why other things are uncontrolled, it's a first attempt to try and do that.
Richard: Why do you want to answer those?
Christopher: Yeah, it just seems like a kind of stupid question.
Tommy: But that--
Richard: I don't understand.
Tommy: The reason why--
Richard: You're either studying low-carb diets or you're not. And if you're studying low-carb diets, you start with a low-carb diet and you build the controls, you build the run in diets, you build everything around that question. If you do something else, you're off in your own world.
Tommy: But I don't think that Kevin Hall is trying to do that because his whole basis is to generate a mathematical model that will predict all this stuff into the future. So if he's trying to look at the effect of purely reducing calories from carbohydrate or fat, then the first step that he has to take to actually truly test this model is to actually do that with everything else controlled.
Richard: I don't see that. If you have to change three variables then you have to do more experiments. But that's the way it is. I mean, you always have the problem in a diet experiment that you don't have three degrees of freedom. You have to or you may have to change everything at once. The way you do that is called simultaneous equations. You don't build your experiment around the model. You build the experiment around what you want to know. You build it on the scientific question. I don't want to give you a hard time but I don't think the study is defensible.
Tommy: Well, what I keep on trying to say is that if you go on the premise that he's trying to do what I say or what I suggest or what he says he's trying to do, then you could almost like -- I'm not saying that the study really adds anything because in terms of real application, it really doesn't. But you could start by -- I almost could say that I could defend the study until the fact, like what Christopher was talking is that he references, he basically rubbishes Gary Taubes multiple times in the introduction and the discussion. And that automatically colors the rest of the study. Then you clearly see that he's set out to--
Richard: That's separate from experimental design. If you defend the experimental design, you must be a nice guy.
Christopher: He is a nice guy. I would say, Tommy, you definitely pursue the truth, like you don't have any kind of low carb bias which maybe me or Richard could be accused of.
Tommy: My only point is that if you start, you have to -- So, what you said--
Richard: I don't have any low carb bias. Actually, my students are always surprised to find out I'm not a low carb advocate.
Christopher: Okay.
Tommy: You said we could take Kevin at face value and assume that his data is normally distributed. But I don't think we can. I mean, I don't even think we can say that.
Richard: We just need to see the data.
Tommy: Yeah, absolutely.
Richard: My new rule is habeas corpus daturum. Let us have all the data. Let us have the body of the data. If you don't do that, you haven't done anything yet. I don't know what the study is until we see all the data.
Tommy: I would hazard to guess that actually if they did the statistics properly, they wouldn't see anything anywhere. And that's what I find [0:39:51] [Indiscernible].
Richard: That's what I think. But I don't know. I mean, one of the things is I actually did the -- The trouble is, I really am not a statistician.
[0:40:00]
So I'm always a little hesitant to -- Because I did convert things to standard deviations. For the change in the fat mass you come out with the total change for each group. Actually, it's in the ballpark of 0.5 kilograms. The standard deviation is about 0.5 kilograms. So that thing is all over the place. In addition, if we're talking about the changes in fat mass, the difference between the low carb and the low fat is 50 grams. Not 500 grams. 50 grams. Each of them have lost at the level of 500 grams.
So the difference in this experiment is 50 grams of fat. Well, I lose 500 grams weight overnight. I don't know how much of it is fat. Probably not too much. Probably most of it is water. But the thing is, it gives you a sense of what kind of number we're talking about. These aren't big numbers.
Tommy: Yeah. Most people will lose that amount of when they go to the bathroom in the morning, at least 500 grams.
Richard: Yeah.
Tommy: So that's the difference.
Christopher: Wow. So is it dead lost? All things are dead lost.
Tommy: One thing that I'd really like your comment on, Richard, was that -- Exactly, if you talk about the fast mass lost, so each of them loses about a pound, on average, both diets they lose about a pound, a little bit more than a pound. But then they say the reason that they didn't see a difference is because the DEXA scan which they use to measure fat mass isn't accurate enough or sensitive enough to measure the difference between the two groups.
So then they say that the most sensitive method for detecting the rate of body fat change requires calculating data fat balance as the difference between fat intake and fat oxidation. Now, the problem is, that assumes that fat is only stored or oxidized, which obviously isn't the truth because fat is used for a number of other processes. But they give no references about why that net fat intake or net fat balance is the most accurate way to detect body fat change. They give no references for that. Have you seen that used before as the most accurate way to detect change?
Richard: No, I haven't. But let me interject, if I can, my general feeling about this whole field. Because this is where we got into it. We got into it because -- Well, I had been teaching metabolism for many years. And I used to teach it, well, I still teach it using low-carb diets as a teaching method because of the role of insulin as a kind of master hormone. So the reason I got into it in a professional way, that is research way, is actually listening to Gary Foster give a talk in which he described this as his 2003, 2002, I don't remember the exact date, the first low carb study.
And he invoked the laws of thermodynamics. And I just finally had heard that too many times. Now, most chemists don't want to claim expertise of thermodynamics but many people including me are definitely interested in it. So that's how we got into this, to show that thermodynamics has nothing to do with this. The laws of thermodynamics need you to expect that a calorie is not a calorie. And when you do find it, it's because of the biological considerations namely the -- In biology, most things are connected in feedback. So you have homeostasis. So that's why usually a calorie is a calorie.
But there's no theoretical reason why it should be. To pursue it, you have to do the kind of fine tuning that Kevin was trying to do but the numbers are too small. You're never going to answer it. That's why I keep telling Gary not to bother with metabolic advantage. Metabolic advantage is theoretically possible. It's explainable by chemistry. If you don't think it exists, don't do it.
[0:45:00]
You're pursuing a chimera. All of which would be okay except we have a way of studying low-carb diets that would change the world. If you did a serious study in diabetes, you would change the face of health in the country and in the world because if you did a good study that people would accept -- I personally don't think we have to do any more studies. You would never use anything about a low-carb diet or never use anything as the first line of attack. Nothing is absolutely predictable in biology. That would be settled.
That's where I think research should be going now. I don't understand why we're basically pissing away a lot of money on a chimera. You understand that I believe metabolic advantage is a serious part of low-carb diets. And I'm not sure that it's worth trying to prove it to anybody who doubts it. The thing is that what I'm strongly opposed to in medical science as it exists now is arbitrary rules. Everything has to be a random controlled trial, all these arbitrary things that are unknown in any other science.
The important kind of measurement is the measurement that answers the question. It could be anecdotal. It could be whatever. So here's a good experiment. Many people -- I don't know about many but several people have tried to carry out experiments where they maintain the weight of subjects on two different diets. For example, Volek was trying to look at the effect on lipids on low carb and low fat diet but he wanted to do it under conditions where there was constant weight. So nobody gained or lost weight. And he wanted to compare low carb and low fat at constant calories.
Which incidentally would have been a much better way for Kevin Hall to do his experiment. But what he found is that people on the low-carb diet lost weight even though he tried to prevent it. [0:47:44] [Indiscernible] is my colleague and he did a human study in advanced cancer patients. Now, of course, these were people who are very sick. So we didn't want them to lose weight. He put them on a low-carb diet. And they lost weight anyway. And even [0:48:03] [Indiscernible] found this.
That's an indication that when you go to low carb, biology reaches a point where if the carbohydrate is low enough, you're going to drift towards weight loss whether your want to or not. And you can say, well, that's not, that's a semi-anecdotal experiment. But it's compelling. You know it is. You know it is if you try to do the experiment and you can't get it to work.
Tommy: So then my question is -- I mean, you're clearly preaching to the choir here. But if we're actually going to make a difference, if we're actually going to turn this into what essentially could be a treatment that can reverse disease for millions of people, you have to convince the people who don't believe it. So then the question is how do you do that? Because you say we can't do more studies but, obviously, the studies we've done so far haven't been enough to convince them.
Richard: I can tell you -- Let me interrupt because I'll tell you the answer.
Tommy: Okay.
Richard: Actually, I'll tell you an answer. There will be a kid who has diabetes and he will have a limb amputated and he will discover that he could have been offered a low-carb diet 25 years ago. And when he goes on it now, everything clears up. And his father who is a very angry lawyer will sue everybody in sight. And then the next day everybody will recommend low-carb diets. That's how we do it in America.
Tommy: But why hasn't that happened already? Because so many people have stories like that. You see them all the time. So why hasn't there been an angry enough--
Richard: It hasn't happened because once you hire a lawyer you have grief.
[0:50:03]
But it will happen if we don't do something else. I don't think it's a good idea to go at the lawyers.
Christopher: So what's the rest of the world to do? Because in the UK, there's no culture of suing in that way like that.
Richard: In America, we sue first and then figure out what happened.
Tommy: But instead, we just -- All the studies we've got so far, obviously, give us enough of an answer to implement it. But then you're still stuck fighting so much institutional dogma.
Richard: Let me -- I'm sorry, go ahead. Finish it.
Tommy: I was just going to say that every time a study comes out in support of low carbohydrate diet, they wheel out the same national health service dietician, so say this is in the UK, to give the same stuff about saturated fat causing heart disease, and then everybody goes back to their previous jobs despite the fact that evidence is overwhelming.
Christopher: Yeah. So I can tell you that's exactly what the National Health Service said in the UK. But you could argue that the whole low fat versus low carb debate is needlessly overcomplicated, just complicated distraction from what these four simple words of advice: Eat less exercise more.
Richard: Oh, great. It's going to change. I mean, let me tell you what I think the problem is. There's an old joke in academic circles that there are three stages of the response to a new idea. The first is: This is wrong. And the second is: There's nothing new in this. And the third is: We thought of this first. So those of us in research are most concerned about the third because it's inevitable now. You can see it every place. It's collapsing. The low fat idea is dead. The question is whether the people who did the work deserve the credit and whether we're going to wind up having somebody redo poorly what Volek has done well. That's a real threat. So in that sense--
Tommy: But it's not harder. It's much harder to bury science and results now compared to even two or three decades ago. The people who know know that Volek is one of the pioneers in the field. So it would be very difficult, don't you think? Once it becomes--
Richard: It was very difficult for them to persist up till now. They're good at it.
Christopher: So what's going to happen next? There was some kind of hint that maybe there was more data yet to come. Tommy, do you think there's going to be a follow up to this paper based on the same study? What's going to happen next?
Tommy: Yes. So you sent me an article, some news article saying, which implied that they'd done some MRI scans or some kind of maybe functional MRI scans to look at activation of various parts of the brain, maybe to look at hunger or pleasure or something. And that's fairly standard practice. If they've collected a lot of data, they want to get as many papers out of it as possible.
Richard: Let me interrupt and say I'm going to write to Kevin and ask him for all the data. You got to see all the raw individual data. Otherwise, you don't know anything.
Tommy: Absolutely. But my theory was that if there's other data, they might be holding it back because it doesn't fit with the rest of what's come out. And that's very common too. So that's a possibility.
Richard: Foster's second paper was borderline fraud because he said that the macronutrient composition wasn't important. But he didn't measure the macronutrient composition. I sort of called him on that but he said he had more data that we're going to publish something else. I don't believe that stuff.
Christopher: And do you think Kevin will give you the data?
Richard: He has to give me the data.
Christopher: He does. I was just wondering whether he has to give it to you or not actually.
Richard: No. It's government funded research.
Christopher: Interesting. So what would you do with that?
Richard: [0:54:35] [Indiscernible] institution.
Christopher: And then so what would you do? Will you write a paper or write something?
Richard: No. I will encourage him to publish it.
Christopher: Okay.
Richard: It's his data.
Christopher: Okay. Well, this has been fantastic. Thank you so much for giving your time to me today. I'm really, really grateful for this. It's been a fascinating conversation. I hope everyone else listening thinks so too. Yeah, I mean, if anybody has got any questions, then please feel free to get in touch and I'll see if I can get those answered for you. Was there anything else you wanted to add, guys?
Richard: No. I'm so glad that you were patient. I've been juggling so many things that I got two appointments confused.
Christopher: Okay. No problem. And you, Tommy, is there anything else you wanted to add?
Tommy: No, no. It's been a real pleasure. It's been great.
Christopher: Okay. Cheers then.
[0:55:28] End of Audio
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