Dr Tommy Wood and Dr Ben House join me on the podcast this week to discuss the process of the science of body composition.
Listen in as the docs discuss
Check out The Bro Research Center at https://broresearch.com/
Dr Ben House at https://broresearch.com/staff/ and on IG at https://www.instagram.com/drbenhouse/?hl=en
Dr Tommy Wood at https://www.instagram.com/drtommywood/
Mon, 2/15 4:21PM • 1:43:24
study, data, paper, gain, mri, body composition, keto, tommy, protein, group, diet, body, glycogen, dexa, individual, control, measure, fat, people, published
Michael Nelson, Dr. Mike T Nelson, Dr Ben House, Dr Tommy Wood
Dr. Mike T Nelson 00:00
Hey there, it's Dr. Nelson here back with another edition of the flexa diet podcast where all things related to muscle hypertrophy health and body composition course. This is brought to you by the flex diet certification which I created. So go to flex diet calm, FL EXDT calm, and there you'll be able to get on the waitlist for the next time it opens up. If you want to know eight different interventions to maximize your nutrition and recovery, everything from carbohydrates to fats to ketones, fasting, exercise, need sleep, and much, much more, go to flex diet calm, click on the get on the waitlist that'll put you on the semi daily newsletter, which is free for like vast majority of my content goes out. And you will also be notified the next time that it opens. And today in the podcast, I have my good buddies from the bro Research Center, Dr. Dr. Tommy wood, and Dr. Ben house. And today we are breaking down all things related to the scientific method. And a little bit on statistics, which don't tune out yet because we'll give you some practical ways of how you can look at studies and at least have a little bit of a determination if something seems kind of funky or not. And we center this a little bit around body composition. And how would you actually measure that? We're talking about a newer study that was released here looking at the ketogenic diet, and can you gain body mass muscle, lean body mass on a ketogenic diet. So we'll break down that study and all of its gory details here and give you some takeaways on statistics, the scientific process, what to look for in studies. So check this out. Fun discussion, as always with my good buddies, Dr. Tommy wood, and Dr. Ben house. Hey, welcome to the flex diet podcast. Today I have a very fun discussion about science. So yes, this will be a fun discussion about science. So don't turn out right away. I got my buddies, Dr. Dr. Tommy wood, MD, PhD, and my good friend, Dr. Ben house, PhD, and we're gonna talk about a little bit about the scientific process. And we're gonna still frame it around some studies looking at changes in body composition. So even if you're listening, and you're not going to be super excited about some of the esoteric science stuff we're gonna get into, hopefully, you'll still learn some stuff about body composition, and what are some of the limits of it? And what measurements to maybe trust and maybe not trust? And Dr. Tommy has a very popular statement he wanted to start off with.
Dr Tommy Wood 03:12
Yeah, Thanks, Mike. For the invitation to be here. And I've gotten over the last year, but it's been a probably an inexorable decline before then, in terms of how I feel about other people talking about science in the public domain. I basically gotten to the point where if particularly like during lockdown, and and all the things that happened around that, and then really want to dig into those specific data. But I've basically gotten to the point where I feel like unless you have the the ability first to access the entire paper, rather than just the abstract on top of it, rather step one. And then after that, you should be able to understand pretty much every word in that paper from the data, how is collected the limitations of that data, the statistical analysis that was performed, you know, how that led to the results, how that then led to the discussion? What were the pros and cons downsides of that entire study, unless you basically understand that entire process, I feel like you're not really in a position to comment on social media or any other or podcast or platform about science. And that probably constitutes 99% of people talking about science evidence base, you know, quote, unquote, on the internet, but just just because, like you see it going wrong, and people being wrong so many times that I feel like, people just shouldn't do it. And it really annoys me
at the end of the podcast
I want your thoughts on that. That's why you're here.
Michael Nelson 05:00
I mean, I'll start off and say that I think it's the the next part of that would be, you have no faith in the journal process and the peer review process, then at that point, because I think a lot of people as an outsider would look and go, Okay, I may not know a lot about science, but I know, huh, nature, ooh, top journal. My assumption going into this is that they probably did their due process, like most papers are rejected from a journal like that. It's probably something of novelty. Hopefully, the peer reviewers are a little bit higher up on the scale. And if it got through peer review at that level, then I'm going to intrinsically trust maybe some of these aspects of the study that maybe I don't understand, or I just don't want to look up.
Yeah. And I mean, that's a great point, because actually, the highlight on that, I
Michael Nelson 05:51
will confess that I have done that.
And of course, I and of course, I've done that, too. I'm not completely blameless at some point you trust maybe you know, the author, you know, their work, you believe it to be a good quality, and then you skip some of the steps, right? I mean, we we've definitely all done it. But it's interesting, actually, the higher the impact factor of the journal, the more likely they are to have papers retracted. Because of particularly like Nature and Science, there are some institutions, some countries where your salary, your like, academic promotion is basically tied to the type of journal that you publish in. So that makes it really ripe for manufacturing data, right. And so like, we are not talking about a keto study in a athletes published in some like strength conditioning journal that nobody's heard of, like we're talking about, like the big players here. But, you know, those guys are much more likely to get papers retracted. Like even now, people are looking back at some of the careers of essentially guys who may go on to get Nobel prizes. And you're seeing inconsistency in the data like, hey, that Western blot, there was supposed to be one thing in that paper, they use the same Western blot for something else in that paper, right? Or, like we looked at, you know, they supply their data online repository. Normally, people don't look at that. But then you look at it, you're like, hang on a second, this is physically impossible. They just like copied and pasted numbers around here. So like, that stuff happens. And so like, this is like, right at the pointy edge of the spear. We're not saying that this is what is happening in most places, but like, you can't necessarily trust all those processes. Because they, you know, they're kind of set up to fail sometimes.
Michael Nelson 07:24
Dr Ben House 07:25
Oh, you're you're in a bad mood, Tommy, I mean, he literally went to like, okay, to track down and favorite, we have to be able to look at the data, we have to know whether it's feasible or not.
Michael Nelson 07:35
And the data may be manufactured.
Yeah, well, I'm thinking about like, my, my friends, I'm thinking about myself. I'm like, if I'm, if I'm looking at papers, I think there are tools that you can use, and heuristics that we can use to be less dangerous when we're looking at papers. And that's seeing like always taking the route of the null hypothesis, always taking that as your as your main frame. And I think where we see people get in trouble is they're taking results out of context, generally in a direction. And then on top of that, they're what I see most often hashtag autophagy, you have to hashtag fasting is they're taking a clinically, like a clinically insignificant finding, that may or may not be statistically significant. And then they're overselling it way past it. So they're taking a mechanistic finding, and then overselling it into the applied literature. And so I think that's where that's where this really goes wrong. And where you could be potentially go less wrong, I think is if you're erring on the side of the No, and you're looking so how I was taught to when I download the actual paper, the full paper, the first the first thing I was taught to do is not read anything. Like don't read anything, go right to the fingers go right, go right to the figures. And then before you read the introduction, before you read the discussion before you read any of the interpretation of the data, go right to the method section. And so then, so that's how I would I would really, if I'm going to coach someone up on how to read a scientific paper, is looking at figures first, are the error bars overlapping because then in that you still have Asterix there. Because that's that's immediately like you and I've talked about that. I one of my favorite things to do with Tommy is just just show him graphs and papers that he's never seen before and then have them look at him on the spot. And we'll probably do a little bit of that today. And so would you would you agree that that's probably a better way to look at things and then perhaps provide some people with with tools so that they can know when they're being duped by the figures?
Yeah, I bet I'm a big fan of going to the figures first because I think that if you can't make a really clear figure if you can't basically understand the the main results of the paper by looking at the figures then the authors didn't understand the data or they've had to do so. kind of serious manipulation to get to the point where they thought they could say something about it. And like this gets harder when you're talking about omix, the gut microbiota, right? We can get into, we'll get into that as well. But like, if you can't understand the data from the figure, and then all I look at the figure and be like, hang on a second, that doesn't quite seem right, and then like, line up with the methods? If you can't do that, then like, at that point, you're probably not Yeah, this is this is probably not a great paper to really hang my hat on.
Michael Nelson 10:31
Would you create your own hypothesis in your head, then then both like maybe a hypothesis and a null hypothesis, where people are just kind of starting out in this process? After looking at the data before you go any further?
Yeah, I mean, I would the things that I'm looking at when I look at the figures, as I'm looking at, like, what are the standard deviations compared to the what's the error? What's the variance in the data come in? So that's like, the first thing I'm looking at, like, does this data even kind of look Make sense? And then the next thing I'm looking at is like, what is the clinical significance of this data? Because a lot of times we'll see this like with blood sugar control, diabetes, and fasting blood sugar went down by one. And then there's, there's, there's Yeah, there's a variance of the data of like five and you're just like, yes, yes, statistically significant, but like in the real world lately, this meant nothing. And this is kind of what you see with the glycemic index is like, you get a meta analysis of what the glycemic index versus glycemic like low glycemic versus high glycemic and then like you're not even controlling for fiber at that point, a lot of times in these meta meta analytic reviews. And then you see the the glycated hemoglobin goes down point one five, like Tommy, how excited are you about a glycated hemoglobin going down point one five,
But then this gets taken by the popular media and they're like glycemic index. Oh, my God. And so that's I think the those are the main heuristics that I use is, is looking at the data looking at the seer deviation compared to the numbers, and then I better know if I'm looking at those data that that data, I better know, like, what is that thing that they're measuring? Like? What are the values of that thing that that they're seeing changes? And a lot of times, like Tommy said, the like, when we get into omics data? I don't know what those things mean. Like, I don't know, I don't know what, like mRNA mRNA content? Like I don't know, what's normal. I don't know, if I don't know what changes aren't even significant when we're looking at these these types of these, the data that we don't know as well.
Yeah, that actually, that reminds us of that exact example, actually, multiple examples you've made sort of come together, I remember, like one paper, which was, like, made a big deal of by some, you know, like, fasting or autophagy. Guru. And there was like, they took some people, and they had some before and after, and they did some fasting. And then they said that fasting increases will talk to you, right? That's what you expect. Now, you like fine. So what was the data that they actually measured? So they took blood samples, and from those blood samples, you have some cells, white blood cells that, you know, have a cell nucleus, right, and then make they're making some mRNA. To make some proteins, right, they're turning on some genes. And what they saw was just like the mRNA, so the expression of a gene that leads to a protein that takes part in the autophagy process was increased. Right? That is not the same thing as increasing otology.
Right, know that paper, and they also broke it down, they collected blood in the am and the PM, and the significance was only in the am not the pm for the genes. And I was just like,
Michael Nelson 13:32
when they were fasting the whole time. Yeah.
It was what like, so I, the other thing that I think we need to acknowledge with, with statistics, and with study design, is fishing with dynamite, and hacking. And so like, you throw enough stuff in the hopper, right. And that's what happens with a lot of these population datasets is like, you got a you got a grad student who's just gonna mine it, right. And if you throw 1000 things into it, like you throw 1000 independent variables in, you'll get something that relates to your dependent variable. And then if you don't actually have to show your stats, you can, you can fudge that. So pee hacking, and then and then data fishing are are potentially big deals, but when you know what those are, you can kind of see it in the paper. Right, right. Tom, you might like it's pretty evident when someone's doing that.
Yeah, I think so. And it's usually. And so there's this big philosophical debate that's still going on. It's I mean, it's been going on for decades, and it's still going on, which is that, should we be adjusting for the number of analyses we're doing? should we really be like, adjusting for multiple comparisons we call it and the reason why we do that is say, so if your P value is point 05. So we're going to say a statistically significant, what that means is that if you're below point 05, is that this is the percentage likelihood that your null hypothesis is actually correct. Right. And so like, that's 5%. I didn't get much lower than that. But what you're saying is if if the 5% level is What you say is significant, then if you do 20 tests, right? One of those just by chance, 20 times five is 100%. Just by chance, one of those could be significant. Even and it's just by random chance, right? There's not, it's not a meaningful effect. So you can see, you can adjust this while you just the p value down, based by based on how many comparisons you're making. And some people say, right, and it kind of depends on what you're trying to achieve. So if you're trying to say, yes, there's a real meaningful connection between these two things, there's like some causality here, then it is really important to start doing that, because you know, you're trying to make a meaningful statement that may affect, like, clinical treatment or something. If you're just looking for signals, people would say you shouldn't do it, because you're trying to generate hypotheses that then inform future studies. And that's fine, too, right? Because you don't want to do what we call type two error, which is basically reject or, you know, accept the null hypothesis when it isn't true, right? Like
Michael Nelson 16:02
throwing out data by air, right? You may have something out there was actually a thing you wanted to know about.
Yeah, exactly. Like there is something here, but because you did so many other types you like corrected for it. And then you actually eliminated what is actually a meaningful, like connection. And so that's an important thing, too. So it kind of depends on the type of study what you try to say. So metabolomics, often you just like, right, you just like looking for something. And so I actually just published it just came out this week, a paper it was metabolomics in a brain injury model. And the statisticians, we will try and look for markers of brain injury. And the statistician I worked with was like, I'm gonna bumper any credit. So that's like, correcting for multiple comparisons. She like bonferroni corrected, like, as much as she possibly could like 120 different comparisons, she made it almost impossible to find any meaningful things in there, just because you'd like she set the bar super high. And like that's, that's, I think that's important. But you know, some people might say, well, maybe you lost some important connections there. But if we're trying to say, this market tells you about brain injury, you need to be really careful about what you're looking for. And so, so I think that's, that's something that we really have to think about is, you know, is that taking part in the paper, if they're measuring 100 different things? Have they taken that into account?
Michael Nelson 17:19
And I think your point about what paper Are you trying to design? And then what is your follow up? Right? So if you're not correcting for things, but you're just looking for, quote unquote, associations as more of a pilot study, then by definition, you would need a follow up paper to say, Hey, we found X, Y, and Z, do XY and Z actually really end up being anything. But I think people take that paper, this maybe just looking for associations wasn't heavily corrected. They're like, Oh, they found these three things. So they they have to be Oh, well, the paper said it's significant. So they must upregulate, a toughie G or brain injury or whatever. And I think that's where people make an error, because they forget the context and the limits of how the study was actually designed.
And that's something right, the the and this this is what I mean, like when you take in the context and the methods and the data and the analyses, right? If you don't know that, how do you know whether this is something you should be? like making a big deal? Oh, no.
Yeah. And I'll say, Tommy, and I've talked about another another really, really fuzzy gray area is to like, so one of the one of the data sets that I collected is we had 100 freshmen in at the University of Texas 1000s of dietary calls to get this data set. We had MRIs, we had like nafld risk, we had microbiome, what just just just a huge sampling of data. And that that data set has had now, I think, eight papers published on it. Every one of those papers has had bonferroni adjustments or used MANOVA. But they have not used min overs for the entire like we're not taught they haven't used the min overs for the entire data set of all those papers published, which is kind of like it's just weird, fuzzy gray area, right?
Michael Nelson 19:06
Yes, there's subsets of it correct.
They're all using the same data set, but different subsets of that data.
is like so. So you blow that up. You think about some of the N Haynes data set, right? National Health and Nutrition Examination Survey. 1000s of papers have come out of this one data set. And like, yeah, within a within one paper, you may do some adjustment, multiple comparisons. But have you adjusted for the fact that you have now looked for 10,000 things from one data set? Look, you know, and like nobody's, you know, multiple adjusting for the fact that 1000 other people have made a paper out of this same data set and like, it's just this really weird philosophical question that I don't think anybody's really adequately answered. Right. And so the vast majority of stuff we're finding in there could just be by random chance.
Michael Nelson 20:00
Yeah, yeah, and I, your even your manufacturing data made me have a flashback to when I was doing my PhD, I got farmed out to the EPA department. So I needed my third paper, I had done it, but the results was to the standard deviation was too high. So I couldn't publish it. And I was doing looking at FMD. So follow me to dilation, do an ultrasound on the arm. And anyway, I couldn't, my advisor said I couldn't use the data. So I had to find a third paper, go to the epi department helped him with all this data collection, looking at people exercising on a treadmill and standing versus CD and all this stuff. So I get all the data, I spent four months organizing the data, analyzing next, there's just reams of data. I do the analysis and brought it to them. They said, Hey, when we did the analysis, we split them into the two groups, we found something that was significant. And so the day before the meeting I had with my advisor to show him this. I started again, going through all the raw data to make sure I did everything correct. And then Yep, everything's right all the rod, I started looking at all of the data now all at once, like back to back. I'm like, wait a minute, what the hell like this, how do you get this much of a jump in this guy to here to here like between trial one and two, but not two, and three, I started looking at stuff. And I was like I don't, I don't know if I trust this like something is weird. And so I went all the way back to my, the guy worked with and said, Give me all of the raw data like everything because it was encoded in folders, so you don't know who the subjects are. And I started looking at the folder numbers. And they didn't put it by number, unfortunately, they put it by initial, which they're not supposed to. There was four people in this study that had the same initials. So I had two s's, I had two L's I had two m&ms. And the data between those four subjects got transposed. And the funny part is, after I got all the right data, got the raw data, redid everything for the next three months, initially with the quote, data that had been transposed between a couple of people, it actually was significant. The data when I put the correct data in, which took almost like two months later to do was not significant. So I had to go back and tell them that hey, this data, I rechecked everything, it's actually not significant. And in my head, I'm like thinking this is great. I corrected it. Whoo, yay, is me. Like, well, we can publish it now. I'm like, What do you mean, you can publish it? Now this is I need to graduate in eight months, or I get nothing like I need this paper. I did the right thing. I went back I redid all the analysis. It's novel, it's never been published before. We just didn't find an effect that we thought we would find. Like, no, it's not sexy enough. There's no journal that's gonna publish it. And so I was like horrified twice, once by I did the thing of correcting Next slide. It's been a night sleeping on it like, Oh, my God, I gotta look at this again. doesn't seem right. And I corrected all of it. And in the process, it costs me a paper and almost my degree, which is just weird. That's a
major, huge major problem sign. Yeah. Yeah. Like that is one, I would say the biggest problem is science. Is that. I mean, you're talking about most major researchers have a drawer full of no findings.
Michael Nelson 23:12
Exactly. Yeah. So how many people are going to redo this again? And to me, I was like, Well, I don't care is that when we found is what we found? Yeah. But you know, as you guys know, like, how many cameras are stat. But if you look at all the studies that have been published, how many of them are basically no no findings, it's by far way less than statistically what you would expect. So that means exactly what happened. It goes in a drawer somewhere doesn't get published. And that's just like the end of the story, move on to your next study.
This happened multiple times during my PhD. And I did actually manage to get something out of it. But But we had, so we had an injury model that I studied mainly in my PhD, and we'd always have a control. Like, if we're testing new therapies in this injury model, we always have a control injury group, so an untreated group. And then we have a control therapy group. So we so like, will control both for the amount of injury and for like a standardized, well understood therapy. And then we'd have a third or fourth group, which was some other therapy or combination of something. And we did this, I mean, I did this experiment, this model every week for three years. And sometimes we would get back a result. And the either the injury was less than we'd expect or the treatment, the control treatment didn't work. And we were like all this experimented work will repeat it. But when that happens 15 times you're like, well, maybe Yeah,
maybe this is like a real
part of that. And so like maybe it's like maybe sometimes a treatment that we think is really significant has this big effect. Maybe it just doesn't have an effect. Maybe that's an interesting thing we need to actually look at rather than just throwing this data out. So actually, eventually I did what I called a meta analysis of all of our experiments. So every time we did we had the standardized injury standardized treatment, we just took those two groups and just look like how variable is the response to this standard therapy in the same lab, people doing the same experiments, the same treatment, right. And it's super variable. And like, so we got to the point where like if you want to, and I said this in the paper eventually, which was, if you want to understand the true effect size of a treatment in this model, you need, like 200 rats per group. And most people are doing eight and publishing it and saying that this is a meaningful result or not. And that's not true.
Michael Nelson 25:30
And that's just because like how often even in like body comp and stuff like we're looking at the same idea, like how often do we hold up some variable? And we we don't even know how variability it is. Right? I mean, we found this out years ago, looking at what the extreme responders, non responders to training and stuff, and think of how many studies if they would have just looked at actual published of the raw data, right, you're looking You see, oh, six people here like to bastards up here, and one guy who actually got weaker and smaller during the study. But if you don't know stats, and you don't know what you're looking at, and you don't have a chart of all the raw data, it's super easy to miss how variable exactly to your point, the intervention, or whatever it is the thing that you're even doing.
And this is where this is where I remember, there was a one sided t shirt, which said, friends, don't let friends make bar charts. And for this exact reason, right, because, say you're trying to, you're looking for people responding to a certain diet or a certain training protocol. Right? If you just have a bar chart with a mean, and standard error of the mean, before and after your body comp, you know, either some kind of lean mass or fat mass or whatever it is you're looking at. Right? You can look at, you can find what's the average effect, but like, maybe nobody actually had that average effects, maybe have two groups of people. Yeah, low responders and high responders. And somewhere in the middle is the mean. But actually, nobody had that average effects. And it doesn't like the your end result doesn't mean anything to anybody. So you need to have the individual data and say like how the individuals respond. And how do we figure out right, that's the interesting question. How do you figure out who's gonna respond in a certain way, rather than what's the average effect?
Mike, can you? Can you let me share my screen right now? Because I can, I can.
Michael Nelson 27:15
Yeah, I did my limits of how I can do this, but I will try to figure it out here. I can make you a host. And that'll do it. Right.
Yeah. Or you can simultaneously allow anyone to share screen.
Michael Nelson 27:28
Yep. So you're the host now, so you should be able to
I so I'm gonna share my screen. See, Facebook? This is so but what about if your friend makes this bar graph Tommy? This is a waterfall plot from that really famous Gartner study?
Michael Nelson 27:47
So yeah. For those on audio burn, you're gonna have to get give us give us the audio description. So you
can you can go to the famous 2018 I think now, Gartner study out of out of out of Stanford, and this is the most recent low fat versus low carb study,
Michael Nelson 28:03
diet fit study.
This is the diaphragm study, okay. And so you can dig into that study, which is really fun. And they they all the raw data isn't isn't online, but you can find these waterfall plots. And these waterfall plots are weight loss by subject between the two groups. And so Tommy, what what do you what do you what do you take away from this?
Yeah, so this, this is actually right. This is technically a bar chart, but each bar is an individual. So I'm okay with that. Right? Because you're giving each and each person's data. And what you basically see is that the pattern, right, so on average, like the vast majority of people, 80% of people just are going to quickly lost weight in both the low fat and the and the low carb groups. And then maybe 10% gain weight. And the pattern is huge. People range from like losing 30% of body weight to gaining
Michael Nelson 28:54
sorry, Well, okay, yeah, for me, that would be the same. But yeah, so losing 30 kilos to gaining Thank you. So like losing closest 70 pounds to gaining 20 pounds, right? But most of them are in the loss. But like literally there are people everywhere along that that line. So like, each person lost a fraction of a kilo less or a pound more. And the pattern is basically identical in the low carb and low fat group. That was one of the best parts of the study is that literally like, the response is identical and the variability response is identical to the two different dietary approaches.
Michael Nelson 29:37
So why do you think the variability is so high then?
The gardener study the free living.
Michael Nelson 29:46
Yeah, the gardener study, they're free living this is diet. This is dietary advice thing like this is I mean, so my I immediately see that that those plots I'm like what is going on in the mind of the people that lost 30 kilos on each side of those diets, like they got to think that this low fat or low carb, they got to think it's absolutely magic, right? And then you think about that other like in all of these people exist on social media like that that whole spectrum exists. And then what about the person who goes on, gained 20 pounds over this, like, with it with a dietitian helping them, it's crazy.
You've seen those, like, there are some people, particularly the anti low carb people, I'm thinking of one in particular, I don't know if they spent as much time on social media as they used to. But basically, they went low carb and gained weight and got super mad about it. And then just like, it turned into their life's mission to try and say that, like, the low carb is Bs, and we should, like nobody should do it or whatever. And that's probably why they were that one person who went low carb gained weight, and we're like, I just don't know what's going on anymore. And they got super mad about it that essentially crafted their entire social media persona
is so this is another problem with people looking at bar graphs, right, is that you're looking at me. And I think I think a lot of people who are maybe we would call them science adjacent, they don't really understand what a mean means. And, and that this is, that's why the Valium trial out of AJC in 2017 was so cool, because they had all their individual graphs on what happened on a low carb and low fat diet. And everyone lost weight in that study everyone, but the cholesterol changes were crazy, like you had like, on average, the low fat group had lower they their cholesterol dropped on average. But there was one dude who went on a low fat diet, cholesterol went through the roof through the roof, like 60% increase, right? And then and then on average, the low carb group was higher fat there, their cholesterol went up a little bit, but you still had folks who their cholesterol went down in that low fat group. So so i think it's it's, it's on me or Mike speak to this is that the difference between needs and individual responses is, is a really, really big deal.
Yeah, that and that's where I think we get into trouble when we then say that something like the meta analysis is the gold standard, right? Because the meta analysis looks at something called the SMD. The standardized mean difference, which is basically all it's looking for is over all of these studies, like weighted in different ways, like different populations, whatever. What's the average black mean difference between the two groups? Right? And so yeah, you can say, low, like, low carb diet makes your LDL cholesterol go up, on average, a small amount. But within that, within all those studies, you had some people were went up a huge amount, right? Probably given you a cardiologist, a heart attack. And some people were it went down a load. And and you when you say like, the meta analysis is the pinnacle of all research, right? You lose all of that human variability. And that becomes a problem as well.
Michael Nelson 33:05
Yeah, I mean, I just think that when you're dealing with something as complicated as humans, the I don't think we understand the amount of variability very well at all period. Right? And when you look and start looking at the individual data, it's just fascinating to me, right? It's all you'll see, seven people go down here, and two people up here, one person up there, and if you're assuming it's not a practice, or how the data was, you know, taken or anything, that it's real, actual data. I always just wonder I'm like, Huh, like, what are the differences between there? And then you think, to how many sort of pretty significant scientific discoveries have been made? of? Why is that person, you know, different? To me? That's a more interesting question. Just for me personally, of if everything is true, the data is accurate, it is what it is manufactured or collected in ere, you can replicate it, that one person who's significantly different than everything else, like, what the heck is going on with that person? And again, but that's not a question. I think a lot of time that is rewarded in academia, when you want to publish a mean, and you want to have sort of like this, I don't want to say standard publication. But I think sometimes those questions are harder to answer, but I don't think there is rewarded as well by the system either. And I think those are the questions that if you actually work with real humans, those are the questions that you care about most. Right? Exactly. And if you work on a one on one basis, that's what you care about.
You care about you care about like so I see a meta analysis that has no finding and I'm like awesome. Everyone else is like they're angry about it. I'm like awesome, because now I know that on a one to one basis, I'm after just finding match fit. And so all I'm trying to do is I'm trying to find the the match fit for this person, whether that's some type of intermittent fasting protocol, whether that's frequent eating doesn't really what it is low carb, high carb I don't like if you don't have Nutrition and Exercise, they get really, really cool when you don't have sacred cows. And you're not putting everyone like you have some, you have some potential pillars that you're standing on. But other than that you're you're really, you're surfing and trying to figure out what, given my experience what could potentially be the potentially work for this individual who, who's in front of me.
I think the another thing that's really important to remember, like, whenever we're applying anything to an individual, right, you're taking some information that was gotten from a group on aggregate, right? And so all you're doing, ever is playing statistics, literally ever, right? So say you have this disease, right? Let's talk about statins and heart disease, we won't talk about the actual numbers, right, but it's a common drug people take it, you like to reduce the risk of heart disease, or, you know, the class of drugs. When you do a randomized controlled trial for statens, right, you treat some people with placebo, some people with the with the treatment with with the treatment, and then you see the response. And on average, like people who take statins, they're at risk, they reduce their risk of heart disease, right? But you have something called a number needs to treat, how many people do you need to treat for one person to benefit, and it is never one. It is sometimes 10 or sometimes 50, or 100. And the stat in treatment is somewhere in the latter end of that, right.
So you're playing a band, you're saying to save, save one heart attack,
we didn't say one off, like you're gonna have to treat 50 people, it's it's gonna depend massively population, population, drugs or drug, but it's in that ballpark. So just like if you're trying to improve some of these body composition, or their strength, or something, any kind of performance metric, you're going to have to apply the same thing to a group of people so that one of them sees that benefit, right? All you're doing is playing, you're always playing statistics, you can never guarantee that one person is going to respond to one thing in a certain way.
Michael Nelson 37:13
You're just trying to, like, I'll just stack the deck a little bit in your favor. Instead of taking the latest craziest harebrained thing that, you know, Buffett, Bob posted on Facebook, you're trying to actually read research and say, Okay, this may not be directly applicable, but based on these studies, I'm probably better hedging my bet in this direction. And then like Ben was saying, Well, what is the response of that particular individual? And then getting further and more granular with that over time, as you kind of figure out what works best for them as an individual?
Yeah, I think protein intake and muscle hypertrophy is a great example of this, right? Like, so we could you gain muscle eating point eight grams per kilogram with with a great training program? Possibly. Could you gain muscle eating 1.6 grams per kilogram? With no training program? Probably not. So like, there's this hierarchy of needs? Like I would probably take sub optimal protein intake? I don't know, I actually don't know. Right? I don't know. I would take what is an optimal hypertrophy program, right? for that individual like, so do you get in this land of optimal good enough. And I think a lot of times in the nutrition sphere, we think that our thing matters so much. But really, it's it's most of the time, nutrition is probably just like this check point. And then it's other variables that are going to be your major driver. Fat Loss is the is really the different one there where nutrition is probably your linchpin being in a caloric deficit. But otherwise, I where I see it, I have a PhD in nutrition. And I see like people, they they think that the nutrition thing is the One Ring to rule them all. And if my it blows my mind that nutritionists think that you can maintain muscle mass and a diet just by eating protein, when it's clear that that has zero effect without exercise, like, it doesn't matter how much protein you eat, if you're not fighting gravity, you're gonna lose 20 to 30% muscle on average, on average.
Michael Nelson 39:13
But that also shows the responses are not are not linear either. Right? You're dealing with nonlinear, you know, systems that don't respond as predicted, because we assume that if point eight was good, well, 1.6, that's gonna be like twice as good. It's like, No, you may have hit that part of the curve where it may not even matter that much. And at some point, it's gonna flatten out at some point, it's gonna go down. But that's, that's harder because we humans want to think linearly and think in that fashion all the time and extrapolate linearly into the future to
the protein. I don't think there's a better place to talk about this and that then protein, right, because it seems like the people that would potentially need more protein are recomp athletes, because that makes sense. mechanistically right. You're tearing your You're building up or people on anabolic steroids. Because you are us, you're building up a lot of protein machinery myofibrillar Protein Protein Synthesis. And so those folks would probably need the most protein. And that's we have, we have for recomb studies, we have Han study, we have long lens study, we have bill Campbell's study, and all of the studies that have found recopying are generally higher protein, gram per pound, so 2.2 grams per kilogram. And we don't actually know, like, this is kind of cool, because we don't actually no 1.6 to 2.2 in that re comp scenario. So now I'm kind of on the fence, like, if I'm going to use higher protein, it's going to be in that scenario. And where I'm actually not going to worry about it isn't an advanced trainee who's probably making mediocre gains, if they're lucky, that are measurable. And so there I would be, I would probably be less concerned, especially if they're, they're in a slight excess of calories. So it's really, really interesting that people that care about this probably are the people that don't need to care about it as much.
So talking about measurable gains. Let's talk about let's talk about priorities paper
first. First, I have to tell you that that one of my favorite people in the world, his sport is how big you can be. And he can't even be put on the dexa.
He can't even do me guessing this is
the same same. But he had to be that he had to be dexus twice. In
half and half, like they Dexter printer, he had to be printed twice.
Michael Nelson 41:41
I think that means he already won.
You can't win, you can't be measured for the thing that you're trying to get. I think I think you might have one.
Michael Nelson 41:49
Yeah. Go ahead. Oh, yeah, no, I
was just gonna say this. This is a recent paper that I Benjen introduced, we talked about it a lot. But it's basically right. If we're thinking about body composition, which obviously, the three of us think about a lot. How are you measuring that over time when you're then looking at some kind of intervention? And again, this comes back to the idea of does what we're measuring mean, what we think it does? And in this particular study, I don't think it does, which basically makes the entire study completely pointless.
I would I would say that the study is not completely useless. Tommy I'm gonna I'm gonna I'm gonna argue just argue just a little bit. And so this this study recently came out it's a it's a keto Gaines study in in nutrients. I have no idea how this thing got passed peer review, I like Tommy and I have I have zero I have zero idea how this thing. I don't know how it got passed. I don't know. And so this this is this study has been taken, I think out of context by both camps. And so this is a game study let's let's just like the backdrop of this study, the positives of this study Tommy aren't enough of them. I probably if they didn't give me height. So that's one of the problem I had to estimate enough of my based on like, normal people. So I put I put them at 510. And these dudes had enough for my route 24. So the, I mean, from us from a study population, this is a very trained study population. Good
title says that competitive natural bodybuilders. So they've got to be like, reasonably jacked.
They're jacked, like, anyone who's just 24 like you, you get them down into the 10% body fat, like these dudes, jack, these dudes like they, they're, they're filling out their the board shorts. And so like, they're, they're also pretty, they're 86. And they're around 90 keys. So they're 1818, probably 180 to 200 pounds somewhere in there. And, and so that's, that's the main positive of the study. The other positive this study is they ran. So it's, it's essentially ad libitum. From a dietary protocol. They gave what they wanted, they assign them at 45 kcals per kilogram of muscle mass based on the energy availability studies. And so these are all good points in the study, we're giving them some love here. And they're eating their self reported data. And again, was around 3500 calories from both groups. Both groups have, you know, they're eating, they're eating a substantial amount of protein at 215 to 220 grams. So the date equated protein between the two diet groups. So that's a I mean, there's another positive study, but that's a bout where the real positives and
so they split them into groups where they have a ketogenic diet group and a Western dog group, right. So they're looking at the effects, the effects of these diets on body composition over was it two months, eight weeks?
Yeah, this is an eight week study. So the the problem there is, I don't even know if you can see gains in this population eight weeks. That's like square one. Like if you have a 20. If you have a 24, from from my, I don't know that I don't know that you can pick that up like it with any with MRI. I don't know if you can pick that up on a week. Would you agree Tommy and Mike? Yeah,
I really pushed it. Yeah, I
think I think you need 16 to 20? For sure. Like, I think I think unless it's unless it's some type of novel training program that they have never done before. But even then, I would be worried about inflammation and edema.
Michael Nelson 45:25
Because it's, I don't probably go away in eight weeks. But you'll see a lot of these like shorter ones that are four. And if you put a novel training protocol on them, I don't know, if you I don't know if you know, if you're getting rid of those monitors, those water games, and so eight weeks, I guess my guess how much control they had over training like you would think a lot,
Michael Nelson 45:48
right? I'm judging by the tone of your voice, not much. was controlled, but
control over training? Not only do they have zero control over their training, he did not even collect data on the training. They don't have. We don't
Michael Nelson 46:07
own anything. So you can't even like retrospectively report what they did.
No, we don't we don't have like the difference in sets per week for the groups we don't have. We don't even know what the hell these dudes were doing. Then we just know their natural competitive body lifters who are lifting we don't know if they change their program, like they said not to change your program. But like we again, we just have no idea. So the main variable, I would say the main variable and making gains as an advanced training is your training protocol. Yeah, that what the hell you're eating? Like, it's not
as long as you're eating enough, or enough calories?
Michael Nelson 46:37
or someone's saying,
yeah. And so both of these diets, check the boxes in theory, for me, your body composition changes, they're eating enough protein, and they're potentially eating enough calories? I think there's I don't know, like, it depends, there's, it looks like there's depending we don't have step counts on them either. Um, I think they're 3500 calories sounds about right to be in a slight excess. But again, if someone's getting 12,000 steps, 14,000 steps, maybe not. And so I was really excited when I when I saw this study, come out, came out Come out, because I this is this is a very interesting question about Tommy and I is can you make gains on a keto approach? And I would say yes, I don't think there's any reason that you if you can get an excess of calories, which is gonna be difficult, right? Because you can be drinking oil of some sort, you can get an extra if you can get an excess of calories. And I'm not pro keto. I'm just, I'm just like fucking pro science. And so like, if you can get if someone wants to do it, if you can get enough protein, you can get enough calories. I think you're going to replete glycogen in 24 to 48 hours, I don't think that's going to be too big of a deal. And so if you're doing if you manipulate your training protocol, I think you can I think you can gain I think you can make gains. And so this is this, this paper is going to be taken out of context by people who are very pro carbohydrate. And so the the, the body composition analysis on this paper was bioimpedance. On an eight weeks. It was research grade biome peanuts. But Tommy, what do you what do you like you have any let's talk about these these markers for body composition. Do you have any hope for what they found? You just tell me a little bit?
Yes. So so what they did found, right is that actually, overall bodyweight stayed the same in both groups.
So they gain tained If anything,
And so, like, things start to get a bit squirrely just when you like, look at the overall like pattern. So like, the keto guys supposedly lost fat mass, statistically significant, it was like a kilo, or one half kilos,
almost four pounds, three and a half pounds of fat on on a bottom penis on a, on a on or whatever they were using.
But they didn't change fat free mass. And they didn't change overall body weight. How does that mean?
Michael Nelson 49:00
So where did it go? Well, it
boggles my mind because, like, man, like, see, this is where we would like this is where you need the individual data because the ketogenic group went like these standard deviation, our show large, right, that you can't you can't even tell what's going on. Because you got a ketogenic group of going from, what is it? lean mass went from 76.5 kilos to 77 kilos, but the standard deviation on both those markers is 12 and 11. Like you have, you have absolutely no idea what is happening.
Yeah. And it's not that that copy normally distributed data, because that would just head away. Like the smallest guy has like 50 kilos of lean mass, like go if you believe the standard deviations of the day was noisy, but it was It can't be that can't be the case. Yeah. So like immediately you're you're not presenting the data in a meaningful way.
I did a Shapiro week. They used it. ANOVA repeated measures.
Yeah, but that's assuming the data is normally distributed. It's probably not home yesterday, you probably got some outliers one way or the other.
So they had to they have to do tests, right? They have to do that. I call it the clock off test was not called cough test. Like I remember having to do all those tests, like weird if it's normally distributed or not.
Michael Nelson 50:19
Yeah, should have done test that said, if it was normally distributed or not, it should have been flagged in that
after testing after testing for a normal distribution.
Who says that they did it? whether it would suggest that shouldn't be the case, because like, somewhere, these guys are losing, or gaining some mass that isn't accounted for by the lean in the two compartment model. Right.
Michael Nelson 50:47
So that's just the air in body count method, then because your air is just so wild, there
was this, this is the main this, the main point was the some of the things that they talked about the statistically significant decrease in insulin from, like 2.2 to 1.8.
like, come on. But that doesn't, just like within the range of that doesn't mean anything. But equally, then that's in the keto group, but equally when so there are other papers, whether if you where you compare, say bioimpedance to MRI, or dexa, and a single point in time, they line up pretty well. Right? But when you're looking at body comp, changes over time, bioimpedance does not track it well, compared to MRI as the gold standard, right? Like the error rate is three to 5%. Maybe. And when you're looking for a 1% change in body comp, right, a one a one kilo game,
let's talk about this, like what, like divide somebody divide one by 77.
It's got to be
like, 1.14, or something.
So even in a best case, like,
well, it's it's 1.3%. So that would be
I think, best case scenario. And this like, talking about an advanced training, gaining 2.2 pounds of like 2.2 pounds of muscle in eight weeks, that seems pretty crazy to me.
Like they must have been, either dramatically change the caloric intake or the training program.
Well, the The other thing that I get mad about all these gains programs is, is adipose tissues, two to 7% protein. So like, eventually, you get enough fat, you're gonna if you're using a dexa, if you're using any not not MRI, or ultrasound, it's gonna look like you're gaining muscle because you're putting more protein on the body, because adipose tissue has some type of protein. And so the problem, the ultimate problem with this study, is that it's going to be taken out of context, in that you cannot gain weight and go on a low on a low carb
diet, lean weight, you can gain lean, you can't gain muscle you
can't like that's how this study is gonna be taken as you can't gain muscle on keto, I think it's probably pretty hard for an advanced trainee to gain muscle on keto, do I think that someone can do it? Yes, I absolutely do think that it's possible. And so, and I'm not like I said, like, I'm not pro carb. I'm not anti carb. It's just this study cannot answer this question in how it's designed. And that's the problem is like, people who are anti keto are going to take this and say that, Oh, another nail in the coffin, you can't make gains.
Because the Western diet group, right, technically, statistically, significantly gained some lean mass, whereas the keto guys didn't. But you know, we can
talk about that. Let's talk about that.
Yeah. So and this is the thing, right? So if you're doing right, there was no information on or not wasn't really standardized. How, like, what what was the setup before? The bottom said they were fasting? No, I
would guess I would guess they had some type of control. Like we don't have a specific gravity on them. We don't know how hydrated they were. But I would guess it was probably fasting early in the morning.
And this is where, right and people have talked about this again, again, the licious or the fact that didn't come up kind of bothers me is that to make a direct comparison, for impedance, right, you're going to have to make sure that they are hydrated in the same way, which if you've been depleting carbohydrates for eight weeks, yes. Got to include some kind of carbohydrate refeed. So
I think you need to stab them. To be honest, like I like because that's the problem with the Wilson study is they they repeated him and she went wild. I think you I think you just account for glycogen. I think you have to
have a biopsy.
I think you have to have biopsy data in these in these samples. I think I think you have to have biopsy data with the with I think that's the only way to do it.
You can look at fiber size, but you're also gonna have to account for liver glycogen because that's gonna like
Michael Nelson 54:59
if you're using body weight and total. Yes,
yeah. Why? Because my, my guess is at this point, right? Yes, they probably going to maintain most of the muscle glycogen, they probably won't be super saturated, like the Western diet guys could be to an extent, but you're going to like liver glycogen is going to be is going to be hugely variable between the two groups.
So we got to Savin twice like that we got to get stamina liver in, which is way less fun than like, if we really want to if I'm like, if we really want to answer this question guys, like, it's gonna take some needles, it's gonna it's gonna take a small needle into the vastus lateralis. Maybe give me maybe give me a Delta bicep. And it's going to take a big needle going after that liver biopsy. I think that's if we want to answer it, I think that's what we're gonna do.
Which is why and so then what's going on comes back to the whole point, which is that like, what are we like, I appreciate your positivity Ben. But what we learned from this study, we've learned,
we have learned one thing, we have learned one thing that's interesting to me, and that is that there were no strength differences. And so they both both groups increase strength without any type of without any type of knowledge of the program. And, and another one that was interesting, another finding to me that was interesting that was not talked about is the reps to failure of the keto group did not go down.
Michael Nelson 56:21
It did not go down. It did
not go down from time point to time point. So those are like, those are two interesting findings to me, like their main finding is completely ludicrous. Don't care about that. Let's, let's take out all your nonsensical data that you use that you weren't that wasn't cool. And we can we can make a new paper. If you put highly trained individuals on a ketogenic diet, and you do not manipulate their training at all, and you test them later for strength and repetitions and failure on a bench press and a squat. They do. All right. I like that. That that's that's that's a that's a publishable paper. I think that's a publishable paper. Yeah. And I think that adds something to literature. This other part, I think that takes away from the literature that we have, and I think it confuses people.
And you did see some light, some I'm not looking at their lipid profiles, like the ketogenic group, like their cholesterol went down a little bit. Their service rates went down a good chunk, 120, right. I kind of know you're working with like, it's unlikely that you're going to have somebody who trains that hard with that kind of body composition where you have to worry about their metabolic health, right. But if you did have to do that, right, this is some way you could potentially manipulate some of that. Right? Okay, there's some potential benefit there.
Michael Nelson 57:35
Another question, if we do stab them, right, so we stab them at the beginning of the study, let's say just the vastus. And then we stab them at the end. We could look at glycogen, we could also look at potentially fiber hypertrophy. But that still wouldn't if we're still using the same methods they did for total body, that still wouldn't answer the question of how much total mass did they gain? Right? Unless there's a way of scaling fiber up to a full body level that I'm unaware of? Yeah, that would answer part of the question, but it would not like for the average person on the street, they'd want to know. Okay, so if I do this approach, am I going to gain one to two pounds of muscle and or not? You know what I mean? From a relative specific question.
Well, that gets into the really the nuts and bolts of body composition and right, it really gets into the the, the actual term of muscle hypertrophy, which we cannot say someone is actually having muscle hypertrophy from a dexa we cannot we cannot actually say that we don't know if it's we don't know if it's fiber volume. Like we don't know that it could be sarcoplasmic it could be these other chambers with a muscle and and Goldie Hawn wrote a great study on that of like, what what what are we actually calling this thing? And I love the kind of there's a there's a summary statement in that in that paper, and it's like, maybe we shouldn't be calling our bridge for hypertrophy. Maybe we should be calling it getting more bigger right? Like you just got you just got bigger. Like that's all we can say is like you got bigger. Great Good job carry on my wayward son. Ah, and and so I think that's what I would use those biopsies for would just be to like cuz the toilet paper in 2016 blew, it blew my mind. It completely blew my mind. And that these keto adapted, endurance runners had were not statistically different in their glycogen right resting state, and their glycogen replenishment eating. They got a day when they gave them strawberry cream shakes without strawberries. Like it was artificially flavored strawberry cream shakes. But no no carbohydrates, like three grams of carbs versus a carbohydrate drink. They actually repeated glycogen similarly to the other group, so I would I wouldn't use it almost like as this check and balance of like, is glycogen even lower in that group? That's what I that's what I would primarily be using that for
you. You just use it as a as a as a variable that you covariant in. Yes
as it is.
Yeah, that makes sense. And so yeah, it was just there, then you can just accounting for variability in glycogen which we think there's there may be some variability in muscle glycogen because in the fastest study the verdict study you're talking about, right? These guys have been eating keto for like 12 to 18 months, at least long term. So they maybe had longer to adapt to this process. Whereas eight weeks may not be enough to do that, necessarily, we don't really know the time course of that adaptation fully. Particularly for glycogen replenishment. So, so there may be some difference between the two, I think liver glycogen is gonna be a bigger deal than muscle glycogen. But yeah, you would just say, if we're trying to see whether the diet affects, like lean tissue gain, you have to and this is how you measure it. This is how you're measuring measuring lean tissue mass or sighs, you have to adjust for glycogen content in some way, even if it's just a statistical Cove area.
Yeah, I think you would want you would want if we let's I think if we design this with physical analysis that we would want, right? We would want a four compartment dexa a four compartment with a dexa, probably a bod pod, and, and we'd also want ultras, I would want ultrasounds too. And so that would be like it like you could this this ideal study, right? And, and then with with a glycogen stab, and then you could really, really start to say, Okay, and then looking at individual data would be the most, I think that would be the most important look, individual data would be the most important, because you really, really just care about how many people make gains at this point. Like, because this this is an advocacy trial, like you're not worried about the mean making games, you just care about if fucking somebody can at this. At this point, we just like, like two dudes, like even if
somebody made a
sound like this not impossible. It's not about like, cuz cuz the, and this is what the one thing that I love about social media is like, because people take untenable positions, they take undefendable positions. And when people take on defendable positions, it's very easy to potentially get them off of that pedestal because all we have to do is all we have to do is show that it's possible. And in this point, for keto, all we have to do is show that it's possible in the published literature, and I don't even think I don't even think that matters, because I think Luis has shown this with keto gains, like he showed it showed it he's shown it definitively that it is possible. And I just feel bad for the guy because he's got his he's got to deal with this.
Michael Nelson 1:02:46
I mean, to be honest, like he's just like, you can't gain mass on on keto. Well, Louise's biceps really do disagree with that. Yeah, I go. Case in point this is Gus is good as a reputation as you need ready?
Yeah, do I think it's gonna do I think it's gonna be difficult do I think it's gonna do I think it's gonna be more difficult to make gains as an advanced trainee when you have a restrictive dietary approach? Absolutely. Because Because the limiter on the nutrition shot side is that like, you talk to anyone in this camp, like eating 30 830 800 calories isn't even that much. Eating 4000 5000 6000 calories on repeat per day. It's terrible. Like, it sounds really, really fun. But let me just tell you like your subjective reality changes and like your jaw hurts. And you just you just like you're just tired. Like, it doesn't matter what it is, like you're using every part of our built food environment, which is like hyper palatable foods, you're just trying to like, to, to not hate food. That's like they like we talked about this all the time. It's like you're literally periodized in your appetite. And you're like, you're you're taking breaks from eating like you're taking, you're taking refeeds you're taking anti, you're taking, taking like you're taking like maintenance breaks is you're so tired of eating.
Michael Nelson 1:04:05
Nothing sounds appealing, again, like Oh, thank god enough to eat any food and I don't have to cook it off to do anything.
It's like we were having this talk. We were trying to start with Eric Helms, right. And it is like you're kind of using the opposite of everything that you would use for weight loss maintenance. Yep. So weight loss meaning is one of the keys is being active right and high step counts, high flux. And so you may want like, if you're really about your gains, you may maybe you I mean you may want to dis regulate your appetite by sitting on the couch and not having any movement snacks. For for a set period of time. Do I think that is the best choice for your health? Absolutely not. But if you're if your main goal is to make gains and break the break the dexa printer
you partially saying that even harder or restrictive diet just because you you have fewer tools, less options. You only think like plant
based let's think about that like plant based you're going to eat Ben's
Michael Nelson 1:05:02
Never gonna stop eating, eating like a dog.
I'd argue the same for keto, who's gonna be bulletproof coffee and chugging heavy cream and some, like face problems.
Michael Nelson 1:05:15
I think pants based problems. Yeah,
I think both of them, you're going to run into gi disturbance, I think both of them, you take those, you try to make gains on those with those it restrictions. And you're just gonna, you're gonna get up into the 80 to 100 grams of fiber, which most people don't do well. And if you talk to people in the bodybuilding camp, like I'm talking like real bodybuilding camp, I mean, you have some crazy shit going on. You have people you have, you have people taking Rifaximin, prophylactically. And people who know what that is like that will blow their mind. In that they are legitimately thinking they're knowing that because their calories can go up so high, they're acknowledging that they will likely have SIBO like symptoms. And it just comes with the territory.
I do surmise is the first time I ever really knew anything about competitive bodybuilding. I read an interview with Jay Cutler and some like random magazine in the UK, like, I know, 20 plus years ago, and he was talking about his dietary protocol, like leading up to the show, and he's like, all I eat is tilapia, and all that comes out. The other end is fish oil. And like, to Ben's point, like at some point, this stuff is gonna happen if you have to work that hard with with IRA. Yeah,
it's just, it just gets a I mean, and we've talked about this, this is done between all between all three of us. Eventually, if you take everything, anything out to his extremes, it's gonna be it's gonna be probably necessarily unhealthy. Like you take a goal, you take it far enough. And that's where you are. You honestly have like the blog, you have the health sphere and the health bloggers who are like, I made that last statement. And they're just like, Oh, my God, I can't believe someone would actually do that to their body. Yeah. Well, you don't understand how you don't understand extreme performance, then you just don't understand what it's like to chase something. That heart and I'm not saying that that's healthy. I'm not saying that that doesn't potentially lead to a ton of outcome identity based issues. Right. But that's what somebody wants to do. Yeah.
It's not good or bad, right? You just I think the only thing that you need is an understanding of the risk and benefit, right, you've got a result of, you know, the the upsides and downsides, then you've made, you're an adult, you've made a decision. And that's what you want to do. They're great all power to you, you should do whatever it takes together, if you're willing to do it.
And so, Mike, and Tom, you have a question for y'all. So if someone is going to try to gain teen or re comp, what, what what would you if you're trying to collect the objective data? What would be the objective data that you care about in that scenario, fairly trained individual or maybe even untrained individual? What would be the data that you would want to collect?
Well, I think you need what you need an accurate measure of training volume. And I would also want either objective or subjective measures of performance and fatigue, right, because if somebody has a huge amount of volume, you can have a negative effect, then if you sort of overtrain in terms of your ability to gain we think that right? Then you're gonna have to have a very accurate measure, I think of intake, total, and individual micronutrients. And then a very accurate body comp measure, including, you know, so probably MRI, if not also biopsies, and ultrasounds and all the other things.
Yeah, my word was MRI, like I have the same worry with MRI that I've had, because I've we've gotten, I got to run 100 MRIs you'd have I don't like it's the marking, same is the same. I think you're going to run into the same problem, because you're going to have to put some type of like, some type of air bubble to mark the spot on the leg or the arm where you would want to cross sectional it. So I think you're going to run in the same, the same problems that I see with ultrasound is how do you know you're in the same spot, time point design point and that that's where like, Brian and I have legit talked about tattoos.
Michael Nelson 1:09:41
That's what I thought just put a permanent mark on their leg or something like that. I mean, if it's not gonna show up on MRI, but
you could put you could put something else there that would spot it on an MRI like that. That's not an issue. You just need the spot. And so I'm legit thinking like when we when we run our bro research Have you studies for real or longitudinals? That might be I might have a tattoo artist. I'm like, are you? How are you?
How are you just got anything you like mid side? It's like a tattoo a black line?
Yeah, you gotta do a black line around, like around the whole thigh though cuz i don't i don't just want like,
Michael Nelson 1:10:18
cross section is gonna matter as well. Yeah, right. So, but I think there has to be a gauge r&r on an MRI for body comp isn't there?
Yeah, I think I think it'd be alright.
Michael Nelson 1:10:32
Enough. So we'll have to look now. I'm just trying to think of what the error percentage would be on MRI, I'd have to I can't think you mean
like the slice? Like, what's the, what is the millimeter of the slice?
Michael Nelson 1:10:43
No, just like you're bringing, like Tommy comes in every day, like Monday, Tuesday, Wednesday, Friday, same operator, a different operator, let's, let's say even the same MRI, and we just put him in the machine and say, well figure out his body comp.
I don't even mean I don't even know if we have that. I don't I can't. I don't think that didn't exist. But I think there's gonna be user error there as well.
Michael Nelson 1:11:05
I think they're exactly but it would give you an idea of what about what percentage you're kind of dealing?
They're laying down. That's the other thing if someone's laying down. So I think I think in an MRI you're only getting because you're getting to get well, you'd have to flip them over. Because you're gonna get hammered. You're gonna hit hamstring squishiness. Yeah. On the backside. What about
But But equally, that's just gonna push out, right? That muscle doesn't go anywhere.
Yeah, you just need volume, you need a volume measure
Michael Nelson 1:11:34
I mean, this is the software to do most of
you just yeah, you just mark the you just mark the circles. And then it calculates all your volume measurements. Yeah, we did that. We did that for nafld. and stuff.
But we must be getting to a point now where, like, basically, software can identify, auto detect the border issue compartments automatically?
Yeah, you have to check them. Because sometimes it will, like, you know, you'll get Phantom stuff. That's what they use. That's what we use for nafld. Because you have to, you essentially have to section the whole liver. And a lot of sections.
What's the like this is I don't write so. So if you're doing like an n equals one, what does it take to see an increase yet? This is always going to be a problem. Why? What's the error in the measurement? right ways. But you solve a lot of these problems by just running a decent study with enough people.
Michael Nelson 1:12:27
Yeah, you have enough money.
Yeah. Like, like, part of the reason why why you should have big group size is to overcome some of this variability. Yeah, it doesn't help you on an individual basis. But what and you should have a measure that you can actually detect the size of the difference that you want, right? Which isn't, m by repeaters. For this.
It gets into the point of like, the people that care about this are the N of one, which may not be fucking measurable. Yeah. That's, that's the craziest thing about is like, the, I don't think that you can know, like, at this point, if someone's fairly advanced, I don't think that you can, you got to, I mean, yeah, if you gained a ton of muscle, you're gonna know, right? But gaining, gaining a kilo on your whole body.
Because of that,
that's gonna be like, I don't know what how you measure that. I just don't, and measuring your fat so how most like so if you think about like an ultrasound or calper, measuring your fat and then using your bodyweight to essentially reverse compartmentalize it? Like, there's no way that's gonna work.
I mean, equally, right, where where does this really matter? Right, if you're talking about like, absolute mass, just in sort of pure mass, right, is we talking just bodybuilding? Right, because for most other sports, there's going to be a performance component. But then in bodybuilding, there's a huge aesthetic and prep component, right? Where actually, you probably don't care whether you know whether you exactly gained two kilos and where it went, because that other stuff is going to make a much bigger difference in terms of how you place in your car and your competition.
So here would be my answer to the normal individual who doesn't have an MRI? He wouldn't be here would be my answer is you track fat via via an ultrasound or via a really good galfer system. And you try to push the shit out of the scale. Right. So you track fat, like your track, you track fat at seven sites, and if you're fat isn't going up. And you have everything else else consistent like your macros are consistent, your training volumes consistent. Because these a lot of these people are robots, and your your scale weight, your scale weight is going up on a weekly average. I think you're making gains, I think it is that is the best. I think that is the best way to do it. And and so without having all these without having all this tech I think ultrasound, fat measurements are pretty foolproof, like I have, we have two of them ever research. And like, they're not pressure dependent, like you can push the fat, it doesn't change that much. But you can, it's kind of like the cross sectional area on ultrasound was really sketchy to me because that's that has a lot that has a huge pressure component and a huge water a demo component to so that you'd have to you'd have to either do at the same time after training, or you'd have your or you'd have to make sure that your hydration status was the same. But the fat component of those the sub q fat now, then you run in I've thought I think I've thought of most of the downsides of this. What if you gained visceral adiposity? Then you're fucked. Like then I don't know. Like, if you've just gained visceral fat, and we can't measure it, sorry, I'm sorry. I'm sorry, if you got an apple because you're trying to make a
boy Wait, if you I mean to do a circumference. So this visceral fat, I would probably say you can measure reasonably well on a dexa.
And so we need a dexa sub q measurements
done. But then also waist waist circumference is a decent? Yeah, proxy. It's like visceral fat. I mean, like, you're not going to be able to tell if it's going up by like, a quarter of an inch. And that could be postural and other stuff. So you probably have to gain a lot of visceral fat for waist circumference to really move.
Michael Nelson 1:16:28
Check at some point though, you know, if you're up like an inch or two, then you're probably like, yeah, let's just say too much crazy food the night before.
Yeah, but you could definitely do abdominal fat on a dexa and that's probably gonna be pretty close
for pale for this for this nutrients. Paley's paper. I actually would have liked some skin tones. More
Yeah, I'm just total body weight.
Cuz some, like if there's thomasin skinfold, Sade stayed somewhat similar and the body weight went down. Like we know they didn't like you didn't make gains. You didn't gain. Like, you didn't make a like there's there's no way.
But what? Okay, so bodyweight goes down. Some of skinfold stays the same. But Can Can you better glycogen? Yeah.
You got you still need the biopsy stem? You? You still need the biopsy? Yeah, it's. So this is where this is where people showing me their inbody results. Is is not like, anybody results are somewhat impressive, right? If you've lost 40 pounds, and you've gained 10 pounds of muscle.
Michael Nelson 1:17:41
Yeah, we have a big change.
we'd all agree that that was real. But where I see people going crazy, is they're trying to track like these minute gains over times where I don't think this technology came can actually measure this stuff. Especially especially in little people. If you're 100 if you're 130 pounds, like if you're 130 pound female, female, like all of a sudden you have to see like you got it you have to see. I mean, I guess the if you're just doing it off percent error, but I think that it's also probably going to work a little bit better if you are bigger. But I could be wrong on that. How do you how do y'all feel?
At this point? Yeah, I don't know that. I don't know. And
Michael Nelson 1:18:25
I don't know how it scales. I would imagine that it Yeah, I don't know. I would imagine a smaller person be harder to detect but that's just a guess.
I think it's gonna be really really hard for us to get for me to be friends with anybody people and get myself a deal though. I think that's gonna be really hard at this point. After after this entire podcast, I think that I've lost my sponsorship. Dammit.
Michael Nelson 1:18:49
I can say I looked at a lot of embodies from another group I worked with and 40% of the time because we do serial testing at some gems. And I would say maybe 40% of the time I spent more time talking him off the ledge of about a point 5% change than anything else really, you know, because of it. And it annoys me to no end because a lot of times gyms when set it up, people wouldn't get the instructions. So it's like a three in the afternoon people are just wandering in and you're just like, Oh God.
Oh yeah, if you don't control for hydration status and the amount of chicken breasts you throw that thing like that square one, like if you're not controlling hydration, they have to be they have to be fasted and not drink anything, or they have to be fasted and drink a set amount of water. And the only way this this those type the only way they have a fighting chance in that particular scenario without big types of changes without huge changes that are going to overwhelm the noise would be would be that specific scenario. managing all electrolytes and and they also I would say they have to be really, really consistent on their body weight scale. Yeah. Because if someone's if someone's life is coming As you're going, you're going invited in body. Good luck. Like just just, I don't even know what you're gonna see. Because I don't know how those competed. I don't even know how those those equations work. But I don't think it's gonna be good.
Michael Nelson 1:20:15
Yeah, I mean, in practice, I use body weight a lot. I tell him just each morning, get up, get on the scale, just because I want. I used to do only one day a week, but exactly for all this stuff you guys had mentioned? Like, they would go out Saturday night, and they do their measurements Sunday morning, and it would be securely and then it's four pounds up. And I don't know if that's a real difference or not a real difference, and then they're mad. And let me just get on every day, we can see a little bit of variability. We're just looking for trends over time. How's your performance? Yeah, I haven't grabbed some circumference measurements. It's It's rough. You know, there's pros and cons of that. And, you know, they're really competitive, you know, send some pictures. And yeah, probably about the best I can get without getting too fancy, I think.
Yeah, in a certain point, it's like, do you look better? Yeah.
The majority of people, isn't that like, isn't that the metric? And then there's some subjectivity where like, maybe they feel better, because they're sleeping better and training better, and then they may look the same, but to themselves, I think they look better. And that's a win, too, right? Yeah, the
only real thing that matters is perception. Like that, like, it's all like, it's all it matters. And if you're if your sport is is say completely aesthetics, you could just get better at flexing. And I could be that could be, that could be the world of difference. Like you could have the same amount of muscle mass that you have before. But you're better at showing it. You're better, you're better at the illusion. And so yeah, it's it's a, I think that it's a it's a really, really cool question overall. But I think that it's a question that we are failing at, in the research literature. I think, overall, I think that this is a question that I have, like, I haven't seen a study that has had a good but then advanced trainees that has had really, really good body count measurements that that I would go to the bank on. In males, maybe I'm wrong about that, maybe maybe I haven't looked at enough that have like enough of them, I have 4424 25 as males, probably females, that would be 21 to 22. And they're in their longtime points, really, really solid body composition. analysis with hydration says, I think I think they're gonna come out. I think that they're just, they're just really hard studies to run. And nobody wants to pay for them. Because the questions like this is a very nuanced question.
out there, like, you say, you think they're gonna come out? Like, where is that gonna come from, where somebody's going to invest the time and money and the population and find the people and get the right measurements and have somebody care enough to pay for that.
I'll be attached to something that a company is gonna it's gonna come from, it's gonna come from some type of supplement research company, I would guess I would guess that's how it'd be back end dim, though, that would be my hunch, or it's gonna come from from us three, finding finding some people and doing some stuff. Or, or other groups like us, like other groups that just want to just because of these, these questions are interesting to them. These questions, these studies aren't that expensive?
Michael Nelson 1:23:23
Like, the equipment?
Yeah, like, the only thing that's expensive is the measurements, right? Like the, it's time, it's an Atlas, it's having access to an MRI, MRI device. So he's like, I've met and Mike's met some of these people to like, I met somebody that has MRI in their basement. Like he's got, he's got an MRI. He's got an MRI three floors deep. Like I meet that guy. And like, all of a sudden, it's gonna happen like so there's, there's, there's, it's not unheard of. It's possible. It's possible. It's possible now. Is it? Is it is it a good use of funds and time? That's why I would probably argue now.
To be fair, the vast majority of federal and private funding and science in my mind is completely wasted on garbage science. So this would be no worse.
Maybe a great way to close this out, Tom. Yeah. Is that like, I am so tired of like, I guess you would call to arms studies that compare different types of dietary advice or different types of dietary strategies. I'm so I'm so tired of them. Like it doesn't you're all we're gonna see is like these isocaloric ISO protein studies, like all we're gonna see is we're gonna see a positive finding and we're just gonna see just a massive no findings come after. It's just like, I don't I don't know, like what Mike talked about in earlier like, finding match fits for people is so much more interesting. To me, then does this work better than this thing? Like,
Michael Nelson 1:25:03
I don't care as a practitioner? Yeah.
I mean, but now you're basically talking about Kevin Hall, who's probably spent $100 million of taxpayer money, trying to figure out whether the insert of the carbohydrate and insulin hypothesis of obesity is correct or not. And to be honest, which it isn't, right. And we kind of knew that. But like, that doesn't mean that people can't lose weight on low carb diets. Right? Like, why would you even care that much? Why would you care is like the output in the individual, rather than wasting hundreds of millions of dollars.
He has, even though he's spent $100 million, I think he's still he's still found some interesting things with processed food. And but like it, I'll give you his main outcome is completely unappealing to me. Like, it doesn't matter, his main outcome doesn't matter. But the most recent one that just got all the press, I think it's an interesting finding that people still gained weight on an energy dense, low carb diet. Like that's, that's very interesting to me, they on an ad libitum, low carb diet, they still gained weight, but because of energy density,
right? Yeah, exactly. So like, I gotta tell you, the SAP and you're going to tell me what we're going to find, we're going to put people on a low fat diet, or a low carb diet, it's going to be ad libitum. The low fat diet has an energy density of 1.1 calories per gram, the low carb diet has an energy density of 2.2 calories per gram, which group is going to eat more and gain more weight?
To lower like, yeah,
we knew that was gonna
be the answer. And it's got nothing to do with was carbs in the diet or not. It's just like,
yeah, that, that that is a complete waste of money. Yeah, he then had to be controlled for Yeah, wow. Yeah, that's true.
That's what makes me mad. That's that I mean, that that study probably was probably a an NIH, cost $10 million.
Michael Nelson 1:26:57
But how does that even get a like, if you go into anyone, not anyone but people and say, here's my setup. Wouldn't that be like a monster red flag to someone looking at the study before you even start and be like, Hey, bro.
I'll take the stance that that's real world.
Michael Nelson 1:27:17
Yeah, that's true. I'll
take the stance, that argument that, like, I'll take the stance that, like this is what people do when they find these diets. Like this is the I'll take the stance like, this is what happens when you watch Game Changers. This is what happens when you read Gary Taubes this is what this is. This is what you do. And this is the because that that is that in and of itself is kind of interesting to me. And that honestly, like when you don't like that's where you can make the argument that not controlling for these things and study designs may actually be more interesting than controlling for them.
Michael Nelson 1:27:50
What if you're looking for external versus internal, depending on what you're looking at,
like real world findings, like the exam, in fact, the intermittent fasting stuff, it'd be really, really interesting. If you if putting you in an eight hour window, if that just spontaneously made you eat less calculus? Yeah, yeah, like that. That's a that's a much more interesting finding, then, oh, if I put you in an eight hour window, if I control for everything, is there going to be a difference in body composition? No, there's not like, there's no, like, we have hundreds of years of research that says Like, that's not going to be a thing. There's nothing magical. If we control for activity, so and then and then you get into the studies that have controlled ISO protein, ISO calories, and then they didn't have step counts, or they didn't control for activity. And then you're like, oh, wow, we didn't control for this other thing. So we don't know what it is. It's really cool. I mean, I just want I don't know.
Yeah, yeah, the low carb thing, right? You're right, this this is and this is the problem. So I mean, it is more of a real world scenario, because when people go keto, keto, they're like, Great sausage patties. here we come. And then they wonder why they can't lose weight. And so yeah, there is a right it's the sort of effectiveness versus efficacy and that is better as a better idea of effectiveness. Right, because that's more of a realized
when you saw this too, like you just see it with every diet is eventually I mean, Javelin, paleo. Eventually, you just let you just get rid of ice and then
everything says that the cookies
the vegan diets, the vegan diet is great and like the vegan diet is is I think, is the hardest diet. I mean, paleo shitty. paleo is really easy. It's hard to like you can be micronutrient division and be paleo for sure. But like vegan is is just hard to do well, like the restrictive nature of vegan. Vegetarian is fairly easy, fairly easy to do well, but vegan is just pretty tough like and to do it. Well. Now, can you do it? Well, with supplementation? Yeah. But I mean, when you go, that's why I think we're seeing this big movement towards plant based or at least a year ago, is because if you go that restrictive, your energy density of your food is going to go down. And you're going like that the ad libitum. So let's talk about because y'all what is the what is the benefit of ad libitum? Low Carb?
Michael Nelson 1:30:13
You're talking about a compliance standpoint.
Yeah. Like what's like, what's the benefit? Like if I take a normal person ad libitum, low carb, what what's, what's the benefit?
Michael Nelson 1:30:21
My bias is just usually you're assuming that your compliance would be better because you're eating things like, you know, high fiber, that type of thing. on low carb, oh, you're saying low carb or high carb? Low Carb? Yeah. Maybe I misunderstood.
I'm low carb, what would be your like you're taking a Western night, and you throw them on low carb, what would be your main benefit of doing that?
We'll say you've removed a lot. I mean, you've removed the processed
Yeah, a lot of processed carbohydrates. And so you're restricting. I mean, to start with any restrictive diet results in caloric restriction on average, because you just remove a number of food choices, you eat less, because you have fewer options. You can make up for it eventually, right with your keto cookies. But that's one but then low carb.
There was a keto pizza I wanted to store yesterday was keto pizza, really? Yeah, those chicken and egg whites is the crust.
That's my smart pizza. So
55 grams of fat.
So chicken breast, chop it up, like kind of grind, like cook it and grind it up, add a couple of eggs, a little bit of parmesan, and then you flatten that out and bake it and it turns into crust. And then you can add some tomato sauce and some meat. And it's basically like 80% protein is magical, but it's like close enough to real pizza, that local pizza
is amazing to me that these these approaches, just started a pizza. Like
I'll make me my chicken pizza, you'll you'll be you'll be able to go down a storm in your house.
I mean, I mean,
I mean everybody else.
So Mike, you wanna you want to you want to wrap this thing up, give it to Tommy and I hijacked completely hijacked your, you're
Michael Nelson 1:32:28
looking good. This is a this is what happens when scientists nerd out. So. But no, I think it was a good discussion. If nothing else, that people are still listening and their eyes and ears glazed over and entirely, just to realize that there's, there's always more nuance, and there's always another level, you can go. And a lot of times in defense of people who do studies if you email them, or you can have a discussion with them. A lot of times they are aware of some of the limitations not all of the time, but especially when you get into mythology, you know, differences of a time trial versus a ride time to exhaustion, or you have to pick one way versus the other. And a lot of times it just comes down to resources. Sometimes that doesn't, that's giving them an excuse to get off of the hook. But, you know, there's probably better ways of doing things and do your time, do your research, try and get the full study if you can. And I think also, it's probably used to just think about who is your filter, because as much as we'd love to sit around and think we're hopefully educating people to do better and hopefully some people will do that. Probably the sad reality at the end of the day is people are still going to go to whoever their quote unquote expert is, but maybe they'll have a little better questions for their expert to answer now and some red flags that may may show up to cause him to do some further inquiry. So thank you guys so much for being here. I greatly appreciate it. And Dr. Tommy if people want to find you or if you prefer to stay hidden. What is your choice?
Yeah, people can check me out on Instagram at Tony wood. And if they really want to listen to Ben and I go deep, deep, deep plugged your course. Yeah, the bro bro research. Currently, the main one we have is advanced blood chemistry for athletes, which actually includes loads of other stuff, gut health, metabolic health, all these other things. And there'll be a longevity course at some point soon. We're gonna start putting that together. But yeah, it's gonna be it's even more of this more graphs.
Michael Nelson 1:34:41
But if you learn everything first based on real data, you're saying
as much as
you think you're gonna die. We actually don't get to tell you these are the things that might matter. You're probably not doing any of them, nor will you probably do them because of that. That'll be fun. But yeah,
oh, this is what people tell you to do to make you live longer. This is why that's not true. There's gonna be a bit of that as well.
Michael Nelson 1:35:06
I, I think there's a, there's a lot to be said for dismantling cognitive distortions that are on social media right now. And so I think that as as researcher, as for sure. So, as a researcher, I'm now thinking in my head before even talking about the study or the study, I'm thinking about that in the study design. Oh, yeah. So like, pay all these paper? I would have thought about this, like, it's not that big a deal in research like that, finally, is it like no scientist is gonna, like lose their mind over that study? We're all gonna be like, Oh, this is very questionable. We're not going to put much clout in it. Right. But I'm, I'm already thinking of I'm thinking of that as researchers, how is how is this gonna get taken out of context? If it's a no finding? Or how is this thing gonna get taken out of context. And so that's where I would be squeamish. That's where I think we need to be better on study design and statistical analysis, I think we have, because we haven't talked about this is that scientists have bias to, oh, if you if you've published 20 papers on a certain thing, and like you don't like, that's a big deal. Like, even though you're a scientist, you mean, you may still have inherent bias. And so I think that, as scientists, we just have to be better in our study design. And then we also have to be, I think, we have to get a lot better at selling no findings. And that's, that's my, that's what
made me think like, we were supposed to be wrapping this up. And I had a thought, I think it's quite important, which is that, you know, you can do science, and you can do good science. And we can't really get to a point where we worry so much about how it's going to, like, appear on social media or in the media that we don't do it. And, and I think this is particularly pertinent. And we're not going to get into the whole thing. But it's particularly important for something like weight based or obesity based research. And there's basically this big push now that says, we can't do obesity research, because it'll be used to fuel weight stigma in media and on social media and like, a stigma on those things, obviously, completely abhorrent. Everybody should feel happy and healthy and sexy, regardless of anything to do with their body composition or their weight. But that doesn't mean that that research isn't important. And so like, yes, it's like, we should think about, you know, how is this study going to be portrayed on social media? But I don't think a good scientist could just stop themselves doing it just because somebody is going to abuse that it's a tricky line to balance.
Yeah, I think you have to, how is this going to be misconstrued? And then being proactive about it in, in how you design the study, but also in how you how you write about it, your discussion, your your, and unfortunately, people aren't going to read your limitation section, generally, they're not going to read your discussion. And also, like, we have to honor that in peer review, like for one of my papers, like, I got told, like my limitations section was almost the biggest part of my discussion.
Yeah, I got in trouble for that, too.
Like you, you can't have a limitation section that just literally says that you're really questioning your own findings in the paper. And I was, like, I am like I am. This is a cross sectional study design. And I think that underreporting with dietary recalls, I think that was our main finding in the study. And I said that in the limitations section. And so I think that we just have to, we have to acknowledge this, but I think the anti diet diet culture is is really where this lives and Tommy's been, he's been dealing with a little bit of this and firsthand, and the canceling, the cancelling of research is like, and so we're not canceling that paper. I think that paper could be retracted. I think how it's written, it could be rejected. But that that's not cancelling that idea, I guess I would say is, is, is that that's we're not cancelling an entire body of research, based on like an objective data that shows that this is deleterious like this is deleterious to your health and potentially deleterious to our entire economy. But that's a that's probably a topic for another day.
Michael Nelson 1:39:29
Then mark and find you people find you then.
yeah, at Dr. Ben house. Um, I don't really post that much because it's like Mike and Mike and I've had these conversations, it's like, you got you got, you're scrolling the gram, and you got two seconds to try to figure it out. It's not gonna happen. So every once a while, I'll post some stuff on there, but not a lot.
Michael Nelson 1:39:53
Cool. Awesome. Well, thank you guys, both for being here. I really appreciate it. I always love chatting with you guys. And thanks. Again, thanks, everyone for listening. Appreciate it like
Michael Nelson 1:40:11
Thank you so much for listening to the podcast this week. I greatly appreciate it. I enjoyed talking to Tommy. And then as always miss hanging out with those guys in person. It's been a little while but hopefully, we'll get back together again at some point in the future. And if you enjoyed this and you want to know all about different aspects of nutrition to help with hypertrophy performance, without destroying your health in the process, go to the flex diet.com website XDT COMM And there, you will be able to sign up to the newsletter, just click the button that says Get on the waitlist, and the next time that it opens, you will be notified. In addition I'll be sending you some semi daily content also, it is more on the scientific side but it's broken down so it is actually readable and hopefully not too boring for you. So go to flex diet.com thank you so much for listening to the podcast. As always big thank you to Tommy and Ben for taking time out of their day to come on and discuss the process of science which is always a fun discussion. Miss hanging out with those guys. If you enjoyed the podcast, feel free to send it to a friend family member Anyone else? Give us a review on whatever you're listening to podcasts on iTunes, Stitcher, etc. that is super, super helpful for us. We've got a couple more guests coming up again in the near future. So stay tuned. Thank you so much for listening. Talk to you soon. outtakes Okay,
good, I guess. But still like with like, there's the when they when you compare, like the different ways to measure change the body comprised in a fixed measure impedances Okay, to measure changes in body count
Michael Nelson 1:42:20
your dancer impedance
that's maybe that's
my wife and I say like, me file me. And the interesting thing that I think what for us is that there's nobody in the in Haynes data like us,
Michael Nelson 1:42:46
like so probably not that surprising is it but decent
muscle mass and low bar and low body fat, like doesn't exist. Like so how anybody can figure out how this how like any of population data applies to the reasonably fit individual I mean, it's basically impossible can't do it.
Michael Nelson 1:43:03
But isn't that just because that's really the only big database and data set we have and especially now with people not being able to do lab measurements like the amount of n Haynes papers we're gonna see like over the next couple of years is just gonna friggin skyrocket.
Yeah, that's a great point that's that dataset has already been tortured far beyond its limits. Particular dietary perspective