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- [00:00]
Eric Helms
Foreign.
- [00:05]
Eric Trexler
What's up, everybody? Welcome back to Iron Culture, presented by the Mass Research Review. I am Eric Trexler. As always, here with me is the Dr. Eric Helms. Helms, how are we doing today?
- [00:16]
Eric Helms
Oh, I've been coming back from the post meeting treks for in person blues. Not meeting for the first time, but getting to hang out. Mainly spurred by the light at the end of the tunnel, which is that we're going to see each other again in Oslo in just a few short weeks. This time I think I'll be on the short stick as far as jet lag because it takes me about a day and a half to actually get to Oslo. It's not a short trip for you, but it's not as long as Australia, am I right?
- [00:51]
Eric Trexler
Correct.
- [00:52]
Eric Helms
Yeah.
- [00:52]
Eric Trexler
So basically I would call this, you know, I feel like in Australia you basically had home field advantage. This is more of a neutral site game where your travel is tougher. But I think both of us have meaningful enough distances and times to cover that we're both going to be a little bit off our game. And so, you know, when we get in the cage and hopefully record an episode, you know, it's. It's just down to pure talent. May the best man win who can
- [01:19]
Eric Helms
operate in a state of fatigue that is.
- [01:23]
Eric Trexler
Yeah,
- [01:26]
Eric Helms
you're pretty, you're pretty skilled in that department.
- [01:29]
Eric Trexler
Well, you know, you got to practice, like you got to play. And so I, what I try to do is stay perpetually fatigued and that way it never sneaks up on me. But no, that, that's been. The big question is having, you know, being over in Australia, coming back, knowing I have a trip coming up to Norway, I was like, should I try to get back on a normal circadian rhythm or is it just anything goes for these two weeks in the middle? And frankly, I just haven't bothered with any kind of intervention whatsoever. I haven't had any morning meetings. And so life has been chaos. But I feel like I have reset on a fundamentally different schedule than I'm accustomed to. But it seems to be relatively normal.
- [02:14]
Eric Helms
I love how you can just dismiss two weeks of work when you're incredibly busy. And with going well, there's no point in doing self care because self care is going to get worse anyway. And that does speak to why you're maybe always in a state of perpetual fatigue.
- [02:31]
Eric Trexler
Yeah, I mean, I'll write a narrative in my brain that makes it sound very. Basically flip it into a virtue, make it sound like it's a very brave thing to be doing and that I should be proud of myself. And I don't know, maybe I'm sacrificing to win. Maybe that's the tagline.
- [02:49]
Eric Helms
It is tough being both intelligent and narcissistic because not only can you maintain whatever behavior that you're going to maintain anyway and post hoc rationalize it, but you can then also turn yourself into a hero for doing it. And I'll tell you what, you're the hero that we deserve, but not the one we need. Or you're the one we need, but that we don't deserve. Whatever the Batman quote is, I'll tell you that. Sure.
- [03:13]
Eric Trexler
Yeah. But anyway, Helms, listen. Interesting times for us. I'm trying on this international traveler thing that you, you seem to do basically non stop and for the last, like, decade. But I will see. I've got three international trips this summer. I'm not going to judge it based on the first one. On my way home, I did have Helms. I thought this was absolutely egregious. Four seats in the, in the middle thing of the plane, right. Me on one end, different guy on the other end. He lays across three seats, a full grown adult and is kicking me non stop and, and farting every 15 minutes. So I'm concerned about his GI situation. He's traveling. So maybe there's some variables in the mix. But in any case, I. It got to the point, helms, I'm like 30 minutes in, looking at my watch, and I'm like, I'm going to die if this is 15 hours of a grown man's toes pressing into my thighs. And so I made the executive decision and just snuck to a different seat on the plane. Didn't alert anybody. I figure if they're trying to dig through the wreckage and figure out who was where, that's a problem I frankly don't care about. You know.
- [04:28]
Eric Helms
Oh, they don't care. If you're on an international flight and you can move, you just do it. Yeah. That's one of the things I have accrued that experience and knowledge. They got better. They got other things to deal with. Yeah. Playing than you moving to another open seat.
- [04:42]
Eric Trexler
Yeah. When you're staring down into the abyss of like, the next 14 minutes of a minor grievance becoming the worst thing in your life slowly, you really do have to just say, I'm going to do whatever it takes to, to just alleviate the situation and whatever the consequences are, I'll deal with them later. So, yeah, I learned that. I learned that lesson very quick. But, yeah, so we'll do the whole summer of travel, reflect on it. And folks, if you don't catch me internationally this summer, this may be your last chance. This may be like, you know how all those old rock bands do, they're like final going away tour, gouge the prices, this is your last chance. And then they do it next summer anyway. This might be the real Eric Trexler farewell tour. So early. But imagine that I'm like a 79 year old rock star who looks very feeble and perhaps on the brink of death. That is how you should be treating these international appearances.
- [05:39]
Eric Helms
And also purposely ignoring all the advice that you could get from someone who has done a decade of this travel and just throwing it to the wind and then distressfully working the whole time and not sleeping and then blaming it on the travel rather than a complete disregard for any attempt to leverage that big beautiful brain of yours towards not feeling terrible. But I'm sure it's, you know, just the travel fault.
- [06:07]
Eric Trexler
Yeah, we'll, we'll see. Well, there, there will be a big reflection in post mortem analysis of how it all went down. But Helms, we got stuff to talk about. We are going to dive into the episode here and today is all about protein, which frankly, as a fitness oriented podcast that caters to lifters in the culture of iron. Theoretically, every third episode probably should be about protein, but we, we try to mix it up for reasons unknown. Frankly, I think people would love every third episode being protein focused. But basically on Instagram, I mean, Helms, you know me, if there's two things you know about me, number one, I'm all over Instagram, I'm posting, I'm commenting, I can't get enough of it. And number two, I like to start all sorts of trouble on there. I love to be in the mix, jump in the fray. And we basically were involved in a bit of a debate going on that kind of unraveled on Instagram. Right. So the short version, you can correct me if I'm leaving out any pertinent details, but basically Stu Phillips made a post about kind of the upper limit for protein utility. I would say, like, here's the kind of upper level that there's really not much of a reason to go beyond this particular protein intake. And in that post it was a carousel with multiple slides and it kind of talked through some relevant evidence. And the reason we got kind of pulled into it is one of the slides was about a study that we co authored. We were two of the three authors, along with Martin Refalo looking at protein in a meta regression context for lean ish individuals in a calorie deficit. In addition to that, I think Alan Aragon made a post about Stu's post, kind of arguing with it, offering a contradictory opinion. And I think Martin actually kind of co authored that post. We talked to Stu, the three of us with Stu and of course we're not going to divulge, you know, details of that conversation. That seems like not the right thing to do. But we did have a chat because, you know, for, for people like you and me, Holmes, we'd rather, if we have a disagreement about something on an Instagram post with someone we respect, rather just dive in and just chat with them directly rather than make a inflammatory attacking post or anything like that. So that's kind of how the situation unfolded. There's these two people with big audiences and big followings who are kind of presenting conflicting ideas about these upper limits of protein. We engage with Stu and kind of talk through the science and we figured out this would be a good day to kind of have an episode where we talk through some of, you know, what comes up in those conversations.
- [08:50]
Eric Helms
Yeah, I'd say the only that that's a good summary from a high level and I'd say the only things that are worth bringing up is two things that I want to give Stu some, some credit for is one, both he and I are shills for, for Big Protein. We are both paid educational ambassadors, what we call the optimum insiders, to give you the inside advantage by optimum nutrition. And despite that, I have tried faithfully to over present the importance of protein. Made my career mostly about it. And I've done the thing you're supposed to do when you're a researcher who's paid the medium bucks to provide unbiased educational guidance on evidence based supplements and third party supplements. And that is to read more into the contract and assume they're going to do some type of favor for me if I also just mindlessly shill their products. Clearly Stu didn't get that memo or is too ethical for it. Foolish move on his part. And unfortunately he's probably missing out on the things that I'm hopefully expecting are going to come my way from optimum, which haven't come yet from, you know, misrepresenting data purposefully by pointing out and maybe even overcorrecting thinking that there's this, you know, 1.6 hard ceiling. So that's the first thing. So apparently ethical guy proved that to be more the case.
- [10:16]
Eric Trexler
Yes, if I May, though, I called my bank last month and I said, instead of this mortgage payment, can we just agree that I'm an ethical guy with a lot of integrity and then we'll just look the other way? And they said, no, you actually have to pay cash money. And then I got right back to shilling because, yeah, the integrity doesn't pay the bills.
- [10:34]
Eric Helms
Apparently not. Neither when it comes to mortgages, nor does it come to going from medium to medium large bucks in the kind of unspoken wink, wink, nudge, nudge. But even though they explicitly said no, Eric, we actually want you to maintain your independence. That's the whole reason we want to work with you as an educator. And I said, I got you. Don't worry, everyone's going to be told to eat all the protein. And they said, please don't do that. We don't want you to look like a salesperson. We have a sales team. And I said, understood?
- [11:04]
Eric Trexler
Yeah, loud and clear.
- [11:06]
Eric Helms
Gotcha. Yeah, yeah. And that. That got an odd response, but, you know, I figured maybe they're being recorded. So, anyway, second piece, a little bit of credit to Stu. The subsequent post we made, or I should say that Martin largely made, that we talked about in our private discussion. Stu was a collaborator on. So he, you know, we got to a place of consensus and endorsed it. He endorsed it and he also deleted the original post. If you're trying to look for his post that originally started it. And I think that is something that is perhaps not only a just a feather in the cap of Stu for being about the data rather than about his ego. But two, I think it's a lesson. I'm not sure it would have gone down like that if we had just started publicly flaming each other in the comments. You know, not having the principle of charity, not approaching the person as though they were rational, and also forcing them to manage the onlookers and random comments that you get when you have a public debate. And maybe you're trying to have a rational discussion, but everyone wants it to turn into King Kong versus Godzilla. So it often does turn into that. So, yeah, if you actually want to have a productive conversation online and change the hearts and minds and maybe even find consensus, I don't know, try speaking to the person. Yeah, crazy idea.
- [12:30]
Eric Trexler
But to be fair, like, that's really not good for growing your audience at all.
- [12:36]
Eric Helms
True.
- [12:37]
Eric Trexler
And so, yeah, we really took one on the chin there. And on one hand, we had a productive scientific discussion that led to a consensus. And then, you know, I think a very clear post that educated people on the topic. On the other hand, we missed a great opportunity to get a huge public fight and then grow our respective audiences. So was it a win, was it a loss? I would put a little bit of both.
- [13:03]
Eric Helms
A draw. Yeah. In the grand scheme. Yeah.
- [13:06]
Eric Trexler
Yeah. So I guess let's dive into. I think we pretty much covered the main claim that was made in that original post and it was basically 1.6 grams per kilogram of protein. That's it. Basically that is kind of the high end where if you're hitting that number, you are saturating and getting the full 100% benefit of a high protein diet essentially regardless of circumstance. Meaning I don't care if you're recomping, I don't care if you have high body fat, low body fat, calorie surplus, calorie deficit 1.6 is where those benefits are maximized. And so, you know, there was kind of talk through some meta regressions by Tagawa and colleagues, talked about our paper and basically it was just looking at, hey, you know, in some of these papers like by Tagawa, it seems like, you know, the benefits kind of taper off. Although we can get into that. The Tagawa, the Tagawa data, it's basically two meta regressions. One is on accretion of fat free mass, the other is on strength. I don't think the Tagawa papers are particularly robust for reasons that we can get into. But the other bit of evidence that I can recall off the top of my head was then our meta regression where we did find some additional benefit at some of these higher protein intakes. And we felt that it was important to do that meta regression in the first place because we felt that relatively low body fats in a caloric deficit may put you in a position where it's kind of a unique opportunity to benefit from some higher protein intakes. Although as I think everyone in the mix would agree, the added benefit there for fat free mass accretion or retention is pretty slim. You know, it's the kind of benefit that would really only matter in the margins if you're someone who's really adamant about maintaining every last gram of that fat free mass. So jump in. What have I missed? Is that a pretty good assessment of the original post and kind of what got us to that conversation?
- [15:19]
Eric Helms
It is. And I think there's only two other minor things I think we should include is I think a big portion of Stu's hesitance and criticism of the data is not necessarily the relationships that have been shown but he is highly skeptical that the DEXA derived lean mass changes are representative of true muscle tissue changes. And I think this is where there is a bit of a question mark. You know, he would put forth that somewhere between 1 to 2/3 of lean mass, depending upon what you're looking at, is muscle tissue. And he is unsure. I'm trying to be as charitable as possible here. If that is a constant and a consistent systematic representation as protein increases. And in some previous discussions he has looked at other forms of data where he has felt it would indicate that lean mass accretion in higher protein intake trials are not necessarily representative of muscle and that it may not be just always the same constant amount. I don't agree with that. Personally, I don't have that skepticism. I do think the default position should be that it probably is a relatively constant amount. And I think you'd have to say, okay, well where is this other lean tissue showing up? And there's actually a article coming out in the soon to drop, probably already dropped by the time this episode comes out in Mass that I wrote about that is addressing this concern that you see that's speculative where when you look at muscle protein synthesis data and whole body protein synthesis data, you see a tapering in some but not all muscle protein synthesis data with higher protein intakes in the short term. And we've both you and I ad nauseum have discussed the limitations of muscle protein synthesis data, but you don't see a tapering for whole body protein intake. I wouldn't say it goes up linearly, but higher protein diets are pretty consistently related to higher muscle protein synthesis, sorry, higher total body protein synthesis. And there was even a paper back in like 2020 speculatively saying is this causing like visceral organ mass to grow and is that a problem? And I've even heard Stu like comment previously on non MPS data like, so why are we consuming higher protein intakes to grow our liver? And I'm like I don't think that's happening, I really don't. And the data would actually indicate that probably is not happening because there was a pretty cool study that came out by Kaz Fuchs and colleagues who does great research specifically comparing enhanced bodybuilders, natural bodybuilders, it's cross sexual, I'll give you that. So it's not definitive as well as recreational controls and finding that there were similar organ sizes between the recreational controls and the natural bodybuilders, but the enhanced bodybuilders had substantially larger organs. So if that growth is Happening the data would at least non causally suggest that the, perhaps it is the enhanced nature, not the high protein nature of what enhanced bodybuilders do that that create that. So personally, while I'm open to the idea that DEXA derived lean mass is not necessarily accurately representing what's happening with muscle, I think there is no reason to believe or strong evidence to believe that there is something different about a fat free mass measurement when your participants are consuming above 2 grams per kilogram versus you know, 1.3, 1.4 or something like that.
- [19:16]
Eric Trexler
Yeah, I was really amazed by that paper that you reviewed this month that because I thought the magnitudes of difference in organ size between natural bodybuilders and competitive bodybuilders, I thought it was astounding. I mean it was looking at if memory serves, definitely heart and liver, I think some other organs as well. Right.
- [19:40]
Eric Helms
So it was liver, spleen, kidneys and heart.
- [19:43]
Eric Trexler
Yeah. And like dude, the differences were astronomical in my opinion. Like we're talking about 50% larger. 50 and sometimes higher than 50% larger organs, which I mean, I guess shame on me, shouldn't be surprised based on some of the autopsy reports that have come out from, you know, bodybuilders that died young with PED use. But I kind of assumed, I guess in my, in the back of my head without acknowledging the assumption in the front of my brain, I think I assumed like well, but these are the people that pushed it so far that they're literally already dead. Right. And so these are kind of the extreme cases. But, but yeah man, I, I, I thought that that data was, was quite remarkable, but. No, it is.
- [20:27]
Eric Helms
And I'll just say one more thing. Dudes were 30, dudes were enhanced, but not the type of enhance that you see in those autopsy reports. On average they're taking about a gram of, of gear. And I had only been doing so for several years to like maybe as like, like we're talking if you go one standard deviation like four to four to ten years at most. Not I've been doing this and I died in my 40s since I was 20. Right. And on IPB Pro levels. So I think it is actually quite surprising. And they were a little bit taller than the natural bodybuilders, but not significantly so. And they did way more of course. And it was almost all derived from lean tissue. We're talking about people who are like 220 versus like a buck 80, 85, you know, so you would expect slightly larger organs but not 50% larger. So I 100% agree. It was pretty striking.
- [21:32]
Eric Trexler
Yeah. Yeah. So. So anyway, back to protein. Like obviously, you know, I think we all agree that DEXA is a blunt instrument for making inferences about pure muscle tissue gains. But when you're looking at it in the context of these trials where you've got, you know, higher protein and lower protein group, they're exposed to the same training program. So if there's any wacky kind of training impact on glycogen and water, it should be kind of distributing across both of those groups. You know, I think there's. I'm with you. I think that we can derive and glean a lot of very useful insights from these DEXA derived estimates of whether you're looking at lean soft tissue or fat free mass. But in any case. Yeah, I think getting down to the core of it, the main.
- [22:18]
Eric Helms
Oh, sorry, yeah. One other thing we should mention.
- [22:22]
Eric Trexler
Sure.
- [22:22]
Eric Helms
So we've got Tagawa, we've got our paper. There was also newness 2022. One piece of that analysis which is often not mentioned is they actually did do a meta regression. They didn't try to fit nonlinear shapes to it like Tagawa did and they did find a linear positive and significant association with higher protein intakes and lean mass. But you will also see an expanding 95% uncertainty variability there because there is fewer and fewer studies at very, very high protein intakes. Quite similarly to there being very, very, very few very high volume studies and how we lose confidence. So they're worth pointing out.
- [23:03]
Eric Trexler
Refresh my memory. When he was talking about that in his post, was he highlighting that increase in variance at the higher range or what was his main takeaway from that study?
- [23:13]
Eric Helms
He didn't address the Nunes data. That might have been because he was an author on it, didn't want to be perceived as biased at all.
- [23:20]
Eric Trexler
Oh, I thought you were saying that it was in his post and that I left that out.
- [23:24]
Eric Helms
No, you didn't leave it out, but I think it's something that should be discussed because it's, I think technically the best data we have in a surplus. It was two years after Tagawa and I think it might be useful to talk through broadly what Tagawa found and why there's some potential issues with the way they analyze things.
- [23:41]
Eric Trexler
Yeah, yeah. Here's what we'll do. I'll start talking about Tagawa, then we can hit Nunes and then we can hit our study and kind of round it out from there and talk big picture what we think and maybe why people who largely end up agreeing on this stuff end up presenting totally different kind of perspectives when you just look at the top headline of an Instagram carousel or reel. So starting out with the Tagawa papers, they had a couple meta regression projects. One was in 2020ish, one was in 2022. The 2020 paper was looking at relationship between protein intake and muscle mass measured several different ways. And then one was looking at total protein intake versus muscle strength. And so one of the things that was striking to me just really quickly talking about the strength one because I think it's a little bit simpler in that one. They basically gave two meta regressions, meta regression models. One was looking at studies with resistance trained participants, the other was non resistance trained. Frankly Helms, can we agree we don't really care that much about the non resistance trained part.
- [24:54]
Eric Helms
Amen.
- [24:55]
Eric Trexler
Okay, so we'll just leave that alone. On the resistance train side, if you look at their paper, Tagawa's paper, basically there's this, you know, as protein goes higher, you get a bigger percent increase in muscle strength. That is great. That makes sense. Then at about 1.5 grams per kilogram, uh oh, the line starts going down and it gets to the point where you would look at the figure and say what is happening? Such that these high protein intakes are driving strength downward.
- [25:26]
Eric Helms
And does it drive strength downward or is it less of an increase?
- [25:30]
Eric Trexler
No, it bends down.
- [25:32]
Eric Helms
Well, I know, but those, but the slope, it's still an increase from baseline strength. It's just lower than higher protein intakes, if I recall correctly.
- [25:44]
Eric Trexler
Can you explain that, reword that for me?
- [25:47]
Eric Helms
Sure. So like the, if the, if the X axis is no change in strength, I don't think the line, I mean if you extrapolate the line, it eventually goes to the x axis.
- [25:57]
Eric Trexler
Yes, basically. Yeah, I get what you're talking about. Yeah, it does not get to a negative strength gains, but it does get to the point where, it does get to the point where eating 4 grams per kilogram according to the figure as plotted is essentially the same as eating like 0.5 grams per kilogram for gaining strength. And what's really important about this, no shade to Tagawa and colleagues, but I felt that their reporting of their statistical methods was really light and that's really important because they used an approach to this meta regression called spline regression. And with spline regression, basically what you're doing is you're saying there's going to be the term that they use is knots like K, N O T, you're going to say hey, we're going to fit a best fit line to this data, but it's going to bend in some places. And there's multiple ways of trying to determine how many bends should there be in this data, where should they be. But generally speaking, in your methods, you like to make that known so that people reading can interpret it accordingly. To the best of my knowledge, or I mean, my best guess, because in these spline models, one of the things that also kind of sucks about them is the, the raw original data are not, it's not like there's a best fit line that is overlaying the actual data from the studies, which is much more common in meta regressions these days. A lot of the ones you see, you'll get what we call a bubble plot where big studies have big data points, small studies have small data points, and you're looking at the best fit line over top of the actual raw data from the studies. And so you can look at the raw data and sometimes you'll just say, okay, oh yeah, that little bend in the best fit line doesn't make any sense at all. Right. Or it looks, it's very different when you see the context of the raw data. Right. In this case, there is no raw data. There's just a line that goes up and then bends back down toward baseline. And so probably what happened here is they used a cubic spline and based on the number of knots that they selected, essentially that downward curve after 1.5 is probably statistical artifacts. And that was one of the things that, when we were kind of chatting. So we all agree that there's not a causal relationship by which going from 1.5 to 2 grams per kilogram of protein is driving your strength downward. And of course, we all agreed on that. So the Tagawa meta analysis, you got to be really, really careful with interpreting that figure because it was not made to mislead. But it can very easily be misleading without that context.
- [28:33]
Eric Helms
Yeah, no, that's well said. And I think, yeah. And even when you look at the, and you have to. Here's a great way to kind of check your biases if you see that and reject it. But then you look at the lean mass gains paper and you're like, oh, well, it does start to taper at 1.3, but it's still positive. So more protein better for, for lean mass gains. You know, if that's, if that's your knee jerk and it's fine if you find, if it is, you know, I have probably an overall positive, you know, bias towards protein you have to kind of apply that same criticism. And this is where I think it's useful to look at the Nunes data and go, you know, they had some nice representation of higher levels of uncertainty in their, in their regression which isn't always necessarily the case in some of these visual representations. And it helps you understand why you can get these statistical artifacts once you get into the higher protein intakes where it's just less explored. I don't know if you feel that's accurate.
- [29:35]
Eric Trexler
Yeah, yeah, yeah. The fewer studies you have as you get to the kind of the tail ends of the observable range or the observed range, we could observe as much protein intake as you can force feed. But when you get to those tail ends and the, the density of the data goes down, you're still trying to fit a line to it but you're getting a much, basically using very rough language. It's like an inflation of the impact of sampling error. Right where it's like because there's so few studies out there, you could have the really high change in fat free master strength, you could have the really low one, but there's not enough density of data to actually get the full picture up there. And so we're going to fit the line to something. But that something may not actually end up representing the kind of true population value at that protein intake. And that you kind of teed me up there going into the other Tagawa meta regression that was actually looking at changes in fat free mass or different indices of muscle mass as they call it. And this was an interesting one because with this one they have a figure, we'll call it figure 2 because that's what it is in the paper and helms. The thing about it is that figure two has nine panels. So I feel like this study is like, I tell you what, you let me know what you think about this relationship and just pick your panel is essentially what it feels like. And of course they were just trying to do a good job being thorough in their reporting and saying well what if we did it this way, what if we did it that way. But the end result is that you can walk away from this and say yeah, I think panel F was really the one that got to the root of the question. So basically what they have is three different levels of covariate adjusted. It's like totally unadjusted, very light touch adjustment and then full adjustment and then they have the other kind of axis or dimension that they're looking at is are we looking at all studies, studies that all had resistance training or studies where there was no resistance training. Right. So combined resistance training or non resistance training. And so when you look at the studies that involve resistance training, you can kind of walk away with one of two takes I think from this one being that protein has a modest but consistent positive impact on fat free mass secretion. And that relationship may or may not taper off up around that, I don't know, just eyeballing it, 1.3 ish to 1.5 or 6 ish range. So you could either say based on one of these figures, it looks like it's pretty basically a straight line. The other two basically look like there's a bit of a taper occurring at these higher levels. And then again we've, we've agreed, we've established iron culture fundamentally does not care about the non training studies where we do see one of those weird bends happening in one of the three figures, but not the other two. Again, this is like I think this figure, if you are going to go conclusion to justification in that order, this is a complete Rorschach test where you get to kind of, you get to pick the, the shape of the line you like and then say, well, I think that one is really the one we should be using. But generally like, if you agree that covariate adjustment makes sense whether you're going light or heavy with it. And if you agree that we should probably focus on the resistance training studies based on the nature of the, of what we care about. I think the Tagawa paper looking at fat free mass actually makes a much stronger case for a, a pretty resilient dose response relationship that may taper at the higher end, but certainly doesn't go flat.
- [33:17]
Eric Helms
Yeah, I think I, I would agree with that. And it's, this is one of those funny things where people really like the morton analysis from 2018 and it kind of says something similar, but it wasn't really justifiable based upon the data that existed at the time time, nor the analysis not having any kind of weighting and the actual quote unquote breakpoint not even being significant. And as much as we cautioned at the, we cautioned the user to not buy into the shape of a cubic spline model where you don't actually understand what it was based upon, how many knots it could have, or where you can't see the underlying data, it is telling a relatively consistent story. And even when you look at Nunes, which didn't attempt to fit a spline model so it can't identify a Breakpoint, you could even make an argument based upon the uncertainty that there may be somewhat of a tapering at higher intakes. And you can also look at their categorical analysis which is fundamentally not as well powered as a mentoregression because it's chopping and changing into different categories that it also seems to indicate there's somewhat of a diminishing returns to higher protein intakes because of the relative effect size between those three brackets. So I think, I think the not strictly informed by the latest highest quality data interpretation is that there probably is a diminishing returns relationship between higher protein intakes and changes in lean mass.
- [34:56]
Eric Trexler
Yeah, yeah, I would get on board with that for sure. But so I think we've covered the Tagawa papers really to the depth that is needed to move the ball forward here. And so the last one we were going to chat about before our paper was Nunes and you've been advocating for it extensively. So I'm going to hand it over and let you kind of, you give the lay of the land there. And I just pulled up the figure so I can chime in as I see fit.
- [35:22]
Eric Helms
Yeah, yeah. So Nunes is pretty good. I think it's the most up to date. It's pretty, it's clear in what they did. If you look through the supplementary files as well where you can find the regression, what they presented as their primary analysis was a categorical comparison. And you can look at the resistance training only or the non resistance trained folks. It's a little difficult to tell. What if you want to figure out how many total participants were included in those two analyses, you kind of have to roughly look at the number of studies and then the total participants in the whole analysis. But we're looking at like 60ish studies, I think something like that. 64 maybe on resistance trained individuals consuming protein intake. And that probably amounts to 2,400 ish total participants. There's a lot of data here. And then what they did in their presented principal primary analysis is they chop and changed it. So they had 1.6 grams per kilogram or higher. They had 1.2 to 1.59 and then they had 1.2 and lower. And if you look at the relative within group kind of standardized mean differences, you see a pretty large increase from below 1.2 to the 1.2 to 1.59 range. And you see another increase. But what you would be what would be described as like a quote unquote trivial difference in terms of it being less than 0.2 in terms of the change or the difference between that mid range protein intake and a higher protein intake. And that's kind of that first hint that you're getting at. Maybe there's this tapering effect. And then if you look at the supplementary files and if you look at the linear regression they did again, they didn't try to do an analysis of breakpoint. They didn't try to do a spline model, which as fraught as that can be, that is probably the best way that you would look to see some type of breakpoint, which as we'll discuss, we attempted to do in ours but didn't find. But what you can look at is they have the 95% prediction interval and the confidence interval, which are two different assessments of variance. And you can see that those get wider and wider as the central prediction line, which is just what would this relationship show as protein intake gets higher? And if you look at the kind of the bottom end of that uncertainty, it plateaus somewhere around that same point. Depending upon that kind of eye test, that Rorschach test, somewhere in the one point something you say 3, 4, 5, 6 or even up to 2 grams per kilogram, I wouldn't argue with you. And I think that is all cumulative evidence towards that same kind of conclusion that at least in a surplus there's probably some point where you're going to be getting a minimal benefit at best if you try to go up higher in protein. But we can't confidently say there's no benefit. It's more about the quote, unquote clinical meaningfulness of said benefit. So that's kind of my take on those analyses. I don't know if you'd fully agree.
- [38:32]
Eric Trexler
Yeah, no, I'm on board with that. Yeah. Basically everything we've talked about so far really kind of triangulates toward and I mean, no surprise, it's analyzing essentially a lot of the same studies anyway. But, but it's triangulating at this idea that like, listen, if you're trying to maximize muscle growth and you're eating 0.8 grams per kilogram, probably try to increase that by 50 to 100%. If you want to go higher than that, knock yourself out. But there's really not a lot of clear evidence to suggest that it's going to be really worth it to go much over doubling from 0.8 to 1.6. Right. And that kind of brings us to a question that you've been chasing down for probably two or three decades at this point, could be four. But your life's work. I mean you first wrote a non meta analysis kind of a. More. There were.
- [39:28]
Eric Helms
It was a systematic review.
- [39:30]
Eric Trexler
Yeah, it was a systematic review.
- [39:33]
Eric Helms
Quantitative summary.
- [39:34]
Eric Trexler
Exactly. I was going to say because there were quantitative elements but not what you would consider necessarily a formal meta analysis. But that was back in what like 2014.
- [39:42]
Eric Helms
It got published so I wrote it in 2012 and then it got accepted in 2013 and it went to press in 2014. And again, this was long enough ago that those things actually meaningfully differed from one another and took time. So yeah, this was my, my master's thesis. So you joke about it being, you know, two or three decades, which it's not, but it is almost 15 years ago. So it's not that much of a joke and that does make me feel old, but it was my entrance into the peer reviewed literature, so.
- [40:14]
Eric Trexler
Yeah, yeah, so. So, you know, coming from a bodybuilding background, I don't want to put words in your mouth but you know, I think you were, you know, from the bodybuilding and powerlifting background, both sports that involve some degree of weight manipulation. I, I think it kind of piqued your curiosity and well, wait a minute, what about the protein needs of people who are not in these kind of clinical weight loss programs, not in these typical hypertrophy oriented caloric surplus or neutral energy balance type interventions. But what about the people who are actually doing the stuff that you care about, which is I'm already lean but I'm trying to get leaner and I want to keep every little bit of muscle I have. And I think there's a, a solid physiological rationale for why there may be some heightened protein needs or some advantageous benefits of pushing a little higher than normal. And so the idea with the refilo paper, as I understand it, and as I potentially misremember it, is let's take another crack at that topic now that there's more research available and we can do it in a more quantitatively driven way.
- [41:20]
Eric Helms
That was exactly it. Yeah. The rationale is pretty clear when you look at mechanistic data and when you look at the indirect research on it, it's all pretty well supported. So mechanistically, and this actually came out after I wrote this original review, when you look at acute MPS and muscle protein breakdown data, there's a couple of instances where you notice that a caloric deficit relatively large, so there's an impact in this type of short term study seems to blunt the amount of muscle protein synthesis you can get as a response to resistance training in Protein, although those two factors can drive what would otherwise be a negative muscle protein balance positive. And then when you replicate this in individuals who are not overweight and are on the leaner side, and it's been shown in men at least, muscle protein breakdown is also higher. So there does seem to be some influence of what I would think is largely a metabolic difference. When you look at other older data comparing obese versus very lean men undergoing total starvation, you see two to three fold differences in markers for using protein for fuel through various ways like direct leucine oxidation, gluconeogenesis. And there's really no change in those with obesity because they have much more substrate availability and probably through signaling the hypothalamus it's going okay, cool, we're going to use business as usual. But in a lean person they're going, eh, we probably don't want to tap into this 6% body fat when we have all this lush, delicious extra skeletal muscle mass and other lean tissues hanging around that we could leverage. And that also corresponds to when we look at. There's a fantastic 2011 meta analysis on body composition changes during voluntary weight loss by the man himself, Hemsfield. And you see that those who are in the lowest, I think, quintile or turtile of body fat percentage for men, they lose more lean mass. And then finally, anecdotally we see the same thing when it comes to coaching natural bodybuilders is you can coast along, maybe even make some small improvements and then all of a sudden you're kind of at risk for seeing drastic negative changes to your body comp. If you try to lose weight too quickly when you're already shredded.
- [43:48]
Eric Trexler
Yeah. Or if you push it too far. I can say as a chronic over dieter, when you are grinding out those last grams of fat toward the end of your prep and you're self coached and all of a sudden the scale starts responding, it's a mirage. It looks like good news, it is bad news because that's kind of my anecdotal experience as a multi time over dieter for shows is that basically you get to the point where you're grinding out just gram, you know, gram by gram, the last bits of fat and then the floodgates open. You're like, oh my God, I'm down half a pound, I'm down a pound. And you start this like almost faster wave of weight loss toward the very end and it seems good in the short term when you're just looking at the scale and your eyes are playing tricks on you. But unfortunately, I got bad news. You're shedding some lean mass at that stage.
- [44:40]
Eric Helms
Yeah. There's only 700 to 900 calories in a pound of lean tissue. So if you're in a large deficit and fat ain't going, but you see a pretty big loss of body mass and you're happy because you're scale hawking not good. And you'll in an objective coach who can, who can look at it would be like, oh, hold the horses. This is not the kind of thing we want to be doing.
- [45:05]
Eric Trexler
Exactly. Yeah. So I touched that stove twice and then finally realized I probably shouldn't burn myself in that particular way. But I mean, Helms, you know how it is, man. You're coaching yourself, your eyes are on the prize. The show's in four weeks and you're saying, hey, lower weight, I must be getting absolutely diced. Who am I to get in the way of that? Right? All right, coaches, exactly. But back to the focal point here. So I think you did a good job laying down the foundation for why this is worth looking into. Of course, Martin Ruffalo kind of led the way with the stats, and I kind of advised him along the way as he was going. He didn't need any of my advice because he's really, really sharp. But basically he did a Bayesian meta regression approach to this, looking at these studies that fit our inclusion criteria, did one meta regression model looking at changes in fat free mass versus daily protein scaled to total body weight, and then another one where protein was scaled to fat free mass, which theoretically kind of makes a little more sense. And in terms of how deep we want to go on the results, I mean, it's a whole paper, so you can go as deep as you like. But essentially what we did was we tried to, as you alluded to earlier, fit linear, quadratic and cubic best fit models to see, you know, maybe there's some non linearity to this data. But ultimately the best fit for both of those models ended up being the linear fit, which basically suggested that with a relatively modest slope, but a positive slope nonetheless, higher protein intakes generally led to more favorable changes in fat free mass within this population. And, and a natural consequence of that linear fit being the best fit was when it came to actually making the recommendations, we did break that sacred cardinal rule and actually tiptoe beyond that 1.6 gram per kilogram ceiling. And so we had mentioned, like, hey, you know, there's some trade offs, I think, obviously biased. I think we did a pretty good job. Navigating some of those trade offs in the discussion and saying, all right, we did find this linear fit. Technically, it spans the entire range of observed data. But here's where we think you're, you know, practically speaking, you're probably getting the most benefit without crowding a bunch of, you know, fat and carbohydrate unnecessarily out of your diet. But listen, that's a very nuanced way to approach the data. It does not make for a good single line heuristic. And so, but yeah, we really did present the data set. That said, actually, that single line heuristic of 1.6 is the limit always and forever. And in all contexts, there actually may be cases where you do meaningfully, you know, meaningfully, in the case of, for example, someone who'd rather take second place than third place in a bodybuilding show, you may benefit from going above that 1.6.
- [48:06]
Eric Helms
That's really well said. And, you know, a little bit of inside baseball I wanted to talk about in the discussion, the potential for there being diminishing returns of going into higher protein intakes. But, you know, like, there's a fine line between speculating as to what the data might look like with more or how does it integrate with previous research, and also faithfully and accurately representing your data without giving people something to run with. That's an inaccurate representation. And I was vetoed, and I think it was the right call. But I will say, like, if we look at all of the data preceding it in the conversation we've had up until this point in this podcast, we have to ask the question, do we actually, if we do believe that in a surplus, there is a point of diminishing returns? Do we think that somehow by going into a deficit, you could actually circumvent the downsides of going too high in protein and go, you know what? If I get into 100 calorie deficit, I can actually eat 5 grams per kilogram of protein and grow more muscle while losing fat than if I was in a surplus. Because that is actually what a strict interpretation of these data when looking at them together would tell you. But this is a really important thing, like the description of the data. And a prediction is not the same thing as reality. I'll say it one more time, as I always do. You know, all models are wrong. Some models are useful. A strict interpretation of every single one of these regressions would be in the example of Tagawa, because that prediction line keeps going, that eventually, by consuming 5 grams per kilogram of protein, you would be losing strength on a weekly basis while you.
- [49:54]
Eric Trexler
You have fully reversed the impact of resistance training on strength.
- [49:58]
Eric Helms
Correct. So 5 grams per kilogram of protein will make you lose strength from resistance training, but not if you don't resistance train. It will also produce no difference in terms of meaningful muscle mass changes in a surplus compared to something around like the 1.6 to 2 gram range. But in a deficit, it will result in substantial gains in lean mass, more than you'd get in a surplus with that same protein intake. Obviously none of those is true and you could even go like way off the deep end and I could use a slippery slope to prove why the predictions are not recommendations. If you consumed 100 grams per kilogram of protein, you would be able to gain astronomical amounts of muscle mass based upon both the Nunes and our, you know, prediction lines if you just kept going. So, yeah, I think some, some of the things that people get hung up on are that we give a range in that paper that goes up to the highest protein intake, that it was included in our model. But that's just because it was the highest protein intake included in our model. Technically we could have, we were recommended less or more because it was a linear fit. So we do have to faithfully report and describe the data, but then also say, like, but what does that mean? And neither Trex nor I, nor Martin nor anyone would recommend the highest protein intake you can possibly consume just because we saw a linear relationship. So you have to interpret this in the context of the real world to some degree.
- [51:33]
Eric Trexler
Exactly, yeah. And I do remember that conversation where Martin and I. I can't speak for Martin and I don't remember his perspective, but I can speak for me. I remember basically agreeing with you in terms of what I think the nature of reality is, but then saying, but Helms, we can't just say, by the way, this will flatten out, because it didn't. We had a conversation where we just looked at the data and said, brother, if you can draw the line where this thing stops being a linear fit, then draw it and tell me where it is. But what I would say is I absolutely. If you ask me to put skin in the game and say, okay, Eric, is there actually in a deficit in these conditions? Is there still a point where the returns begin to diminish? I would say yes. And if you asked, where is it? I'd say, I think it's higher than 1.6, but not by a ton. Like, if you absolutely force me to, I say, we're probably going to be closer in the 1.9 to 2.1 ish. If I had to pick, that's where I would put it. But we just didn't have the data in that higher range to actually say, and here's where things started to bend. And so that's why, you know, I think when I was younger, getting in the game of research, I would look at some of these studies and be like, oh my God, we're doing that, that old one again, the same kind of study, doing it over and over. But nowadays I look at it, I'm like, can we just commission 40 labs to just go start studying resistance training with intakes between 2.5 and 3.5 grams per kilogram. Because we just need to fill in that big empty space at the top of this range here and see if things really do start to flatten out. I believe they will. But yeah, yeah, we had a really good constructive internal debate about how much of this discussion should be allocated to how we reconcile this with prior data. How much should it be allocated toward our view of the protein world as we see it, and how much should just be faithfully reporting what the model indicated. And I think we struck a decent balance when it all came together.
- [53:47]
Eric Helms
No, I would agree with that. And the nuance is challenging to get across in a paper. And then how it is, you know, obviously taken and ran with, you know, on the Internet is a whole nother thing. And you know, like we had a bit of a back and forth and debate with, with Menno, who was currently entrenched in a debate with, with, with Milo. And you know, so it's no surprise that, that now we're, we're getting a little bit of round two of that with, with, with Stu Phillips, Alan Aragon. And I think the good thing that I will say and all the names I just mentioned is that they're all trying to faithfully interpret the data. And in the thing you just pointed out, in the face of insufficient data to have a strong consensus, there's not a consensus and different interpretations will shake out. But then I think the one thing where we do need to give a bit of a hat tip to Stu Phillips is if you did pick 1.6 as your number, that would be fine for most people, you know, and like, in my case I would probably pick 2 grams per kilogram, but I want it to apply to my people who admittedly are 0.01% of the people who eat protein if that high. You know, like lean people who are lifting weights and super serious about having as much muscle mass as possible in all conditions. And, like, there is a cost to trying to eat 2 grams per kilogram, literally a higher cost, because protein is expensive and getting more expensive. And it is also a barrier to entry for a lot of people. I mean, sometimes I reflect. So Barb and I go to breakfast, my wife and I, Barb, every morning. And we've had really positive influences on each other's lifestyle and DIET in the 21 years we've been together. She eats a higher protein diet and I eat more ethically because of our interactions. But one thing that both of us have not done for decades is to consider something, a meal that doesn't have a protein source in it. And when you go to breakfast, that changes the way you see breakfast. And most people would be totally fine getting, especially in Commonwealth countries like New Zealand, toast and jam or just some porridge, oatmeal, Right. Or a bagel. Right. Or just a pastry from the bar and a coffee. And I look at that and I go, that's a completely unreasonable meal. Let me get back to my porridge with a side of salmon, which every. Like. Which I get, unless I'm a regular at a cafe. The double take every time, you know, yeah, I'm the weird one. So, yeah, 100%, yeah.
- [56:32]
Eric Trexler
And I think that probably gets to the final kind of discussion point on. In my mental model of how this episode would shake out, which is when we have these folks who are all looking at the same data, and it is what I would consider a fundamentally incomplete set of data. The question is, how do you resolve the quantitatively unresolvable question at the higher end when the data start to get sparse? And I think what we're seeing is people who, without that really robust quantitative answer to fall back on what we all naturally kind of gravitate toward is kind of one of two things. Number one is, who am I talking to? Who is the group that I serve and think about when I interpret data? And so for you, that's people who, again, are distraught at the idea of losing a few grams of lean mass and are willing to do in terms of dietary stuff that's outside of the box or a little more cumbersome, or burdensome, willing to do more than most. And so you're thinking it just totally shifts the kind of cost benefit ratio calculus that's going on in your head versus, I think Stewart acknowledged his kind of messaging that he went with in that post was really more catered to your general population or you're a lifting Enthusiast, but you're an accountant, right? You train, you train four hours a week, you're never stepping on stage. The difference for that person of a kilogram of lean mass may or may not be discernible to them. Right. And so what we're talking about in these margins is an amount of fat free mass that will never appreciably impact them in a meaningful way. And so I think that's what gets all these different voices and perspectives coming with these different angles and interpretations of the same general set of data is again, number one, who do you cater to or just who are you? How do you view protein just for your personal application? And then number two, I think another layer on, I think that's the main one. Another layer on top of that I think is what is your approach to the communication or dissemination of information. And I think some people really differ in that area. And my thoughts on that have evolved over time. I, I do, I'm not going to like assign how people view this because I don't know, it's, it's kind of an internal kind of dialogue you work through when you're putting together public facing information. But I think I fall kind of far on a side of the spectrum where I don't adjust my messaging based on forecasted impact. And what I mean by that is if I say what I believe about this protein relationship and it ends up promoting a higher intake, I'm not going to censor that because I think it puts up more barriers to fitness in a way that's going to discourage people who may have otherwise started their first day of their fitness journey if they thought that the maximum amount of protein should have been 1.6, but now I said 1.9. I don't think through that like seven layers kind of set of consequences because I think there's just way too many leaps in the logic there and way too many assumptions. So I've gotten out of the business of saying here's the nuanced take that I think is true. But here's the one that I think is maximally approachable and I'm going to go with the approachable one. And I think everyone falls on a spectrum of how, how many second and third and fourth steps deep you get in terms of the ramifications of if I say 1.6, what does that say to the world? Does that make sense the way I laid that out?
- [60:25]
Eric Helms
No, absolutely. And I think I probably fall towards the spectrum of, for my audience or the audience I'm talking to. Let me think through maybe second order effects. Right. But the difference in what I do and what I'm unwilling to do, that a lot of people kind of just bake in is just going with that and not actually letting the person know that you are doing that. Yeah. And I think that causes additional effects, you know, so someone, and I'm not, I'm not going to say this is what Stu is intending to do or that he believes, but let's say someone said 1.6 is the ceiling, but they didn't actually believe it was a hard quantitative cutoff, but they just felt the marginal differences between 1.6 and anything higher weren't worth mentioning. The inter, the misinterpretation of that I see causing other problems. Right. And I've seen that enough times in my game time in social media, which is across multiple platforms in decades to where I'm very happy to give my opinion or my. Yeah, I think this is the relationship with volume. But I am concerned that you're going to apply this in this way. So instead of me then trying to think about, I need to counterbalance the scales, which I think is too black and white and reductionist, man. There's a current high volume message or there's a current low protein message. So I need to over emphasize the protein or overemphasize or under emphasize volume. No, the data are what the data are. However, there is a current trend I'm noticing of people being too high and blank or too low in X and that might be impacting it in this way. So here's what I think you need to be aware of. But I, I try to be fully, you know, transparent and when I do that, and that does require a longer message or more nuanced message and it does of course tank my engagement to some degree, which is a catch 22 and that I'm trying to reach the people who are, you know, like following these types of trends. But that is like what I've tried to get as good as I possibly can at is still being approachable, at least for certain people or to try to educate other educators on this to try to get the message out. But it is a challenge and I completely know what you're talking about and it's an internal dialogue that I have always. And for me, I haven't settled on anything and I probably ever won't. I'll just kind of have to keep thinking about it because the nature of social media education attention spans and the audiences I deal with are evolving and vary. Yeah.
- [63:01]
Eric Trexler
And that's the thing is, like I said, I don't go into other people's mind and say that they're censoring their message to make it more sticky or to assume what are the priorities of whoever may be hearing this. But I understand why people go that way because, I mean, yeah, like if you were just taking a totally like utilitarian view of this, you would say the, the way for me to help the most people is to come up with the catchiest, most sticky heuristic and package it in a catchy way that grows my audience, loses nuance in the process, but reaches so many people and gives them the lion's share of the benefit of a high protein diet that when it's all said and done and the dust settles and it was worth it because it maximally helped the highest number of people. But I don't know what this says about me, but I just can't. I, like I said, I understand the utility of it, but I still fall very far in that spectrum where I'm like, I gotta call the balls and strikes exactly the way I see them. But like you said, have those caveats of I'm not going to tell you what you should prioritize or, or let me say, I'm not going to assume what you prioritize, but I might give you an indication of what I think you ought to be prioritizing. Like my view, I might be talking to a given population, like you said, and say, so here's the relationship. I think that the example you said with volume is great, Right? Because we can say, well, here's how I think it is, but you're a parent of three and you have a very demanding career and you have a sleep disorder. Do I think you right now need to be doing 40 sets a week? I do not. Right. So yeah, there's always that contextual layer that you have to apply to it. But the ideal, the goal in my mind is not to lose the granularity as you're going for the simplification and to be totally. I think that the least controversial thing you could possibly do in a podcast is talk about public health messaging and Covid. So I'll do it. But that to me was the first time I actually started really wrestling with this is because I think Helms like, like a lot of people who are really entrenched in the science and were actually like reading like peer reviewed papers as they were becoming available to me. I was very intrigued by just what is the right thing to do here? Is it to go with the simple catchy phrase that everyone can latch onto and can be put on the signs and say you need to do X, Y and Z to prevent transmission of COVID Or is it more effective to give the full depth and nuance of the details? But then what happens when people run with it and what happens when people misinterpret it? So I'm not going to actually highlight or comment on what I think was done well or poorly because I actually don't want that shitstorm in my life. But I will say that was the first time I actually started thinking about this. And what is the cost and the benefit of shaving off the nuance in the margins? And I think, like you were saying, I think there's actually costs on both ends. There's costs of not doing it, there's costs of doing it. And like with the protein thing, again, what is the impact of when I do shave off those margins and say they don't exist? What's the impact of that misinterpretation, getting out into the wild?
- [66:13]
Eric Helms
No, I think that's very well said. And one thing I'll put out there too is that this can differ pretty notably based upon what you see when you look in the mirror. If you look in the mirror and you are actually seeing the CDC or you are seeing the government of a large company that provide, sorry, government that provides public health information, this is something to seriously consider. And that is because, you know, although it's been heavily eroded, you have a certain level of authority that can't be superseded. Right, right. Or that a large segment of the population will say, oh, there's probably a reason they said that. These are the ones who have committees and scientists and billions of dollars to throw at that and have thought about public health messaging, et cetera, et cetera. And you know, even then it doesn't always get it right. But here's the advice, like, you know, fluoride in the water, probably a net benefit. Good thing. Do I want to get into the weeds? I could find that information, maybe the CDC website, but their public facing messaging is going to be simplified and a heuristic. Most people understand that, some people don't. But even if the people who don't follow that advice, it'll probably be intended to be a net benefit and is the best information, hopefully that is available to that organization. Like I said, not infallible. I'm just saying. But no one's going to come in to the Instagram feed and then just trick you or make you change your Beliefs. There's a difference between you and I doing this and here, and here's where I think people I actually, because if I really did think doing that was better, I would do it. But I don't. And here's why. I can position myself as an authority. I can position myself as the, the most trustworthy person. And there are some people in my audience who will follow what I say because for, for them, I'm their chosen information provider for this stuff. But there's a tremendous number of people who, I'm one of many, I'm the one they're listening to now or they are casually coming across me for the first time and there's going to be somebody with, you know, a little more confidence, slightly more attractive, slightly bigger, an extra MD after their PhD or whatever. They can position themselves as a larger authority or they just have more social proof in terms of social media. And if they tell you something different, you're probably just going to have to go, well, I don't know who the real expert is, but I'm going to go with the guy with the slightly bigger biceps who also has, you know, the things that Helms has or what have you or I like the cut of that guy's jib better. So the reason why I don't play this game of thinking, you know what, I'll give them something close enough to the truth to not piss off the rest of the evidence based crowd to where they need to take shots at me. But it'll hit the largest people with the best social media algorithm engagement to have the largest change is that is a falsehood. 20 minutes later they're going to get hit with something else, someone else using the same strategy and maybe the same even, even rationale or something totally different. Like I don't give a shit. I just want to make something go viral or sell you a book and I'm hoping to get the impulse buy and it'll just be something else where they get caught up in that slipstream. So for me the nuance is important because I want them to see the difference. I want to try to also subtly build critical thinking skills in my audience because I want the message, even if it hits less people, to stick rather than it just being the right piece of information in a sea of other information that is anywhere from completely wrong to maybe even slightly more right than what I said.
- [70:01]
Eric Trexler
Yeah, I hear you, but the reason I bring this up, I stumbled across a paper, it's called a taxonomy of non honesty in Public Health communication. And it's a really fascinating read. It's like a fun one. It's probably not fit for public consumption because I think naturally, just by, just by its general premise, it sows distrust in public health authorities. But it's in the Journal of Public Health Ethics. Right. So it's what people who are in that industry when they're talking shop. It's kind of the ethics of when we simplify how much is too much. At what point have we simplified to the point of distorting? Right.
- [70:41]
Eric Helms
Yes.
- [70:41]
Eric Trexler
And so it talks through like very. It actually names specific versions of this whole spectrum of non honesty slash dishonesty. So it's a fascinating read. If you're looking for a nighttime put down your novel, read this instead. It'll be a fun ride. But no, I agree. I think I, I really. The way I view public communication of science is, I mean of course we, we run mass, so we're extremely biased. But it's not just let me give you the answer, but it's also let me give you the process and show my work. Because you're going to have a thousand questions that come up in the next year. And ideally it'd be better for you to start thinking through kind of similar thought processes and challenging your assumptions in similar ways. And only when you show the work can you actually help people kind of start building that skill. So like for example, I thought the Tagawa strength finding was such a good example of hey, that's in your face right now. It lacks face validity. What the hell's going on? That is like the intro to a lesson in a classroom setting. Right. Why would this be the case if no one here believes it to be true? And you start talking through the stats, you start saying, oh, so the statistical decisions that are being made in all these regression projects matter. And so. But they're all looking at the same underlying data. What does it mean that what's happening on the tail end of the data isn't exactly the same in each one of these? Right. So again it's that it's when you start thinking through the like through the professor lens of how would I actually do something interesting at the beginning of the class to get people bought in and then have a compelling lesson. Yeah. I find that to be the more rewarding side of science communication. Again, all the bias infused because I teach for a living. So that makes sense. But, but yeah. So I think just to wrap things up here, I thought that the way it all unfolded, you know, Stu made a post we agreed with 90% of the take home point. But then we had a direct conversation rather than a big, you know, public shitstorm. We reached clarity, we reached a consensus, collaborated on a post, high fives all around, everyone happy, mutual respect, and the game moves on. And I think that that's a really good model for how this could work. Although Helms, as we've said many times, a very boring model. Not good for the bottom line.
- [73:11]
Eric Helms
No, if, if we're talking maximal social media engagement, not doing well. And I think the final thing I'll say is, in all seriousness though, there is a difference between you said, hey, science communication versus influencing versus public health messaging. They are different. You're not a science communicator in the other two categories. You are communicating public health information that stemmed from science. And then you have to think of all the ethical issues and communication issues that, that was discussed in that taxonomy paper.
- [73:46]
Eric Trexler
Yeah. And in their case, it's literally how many people die if I say this wrong, which is not, not what we're used to.
- [73:53]
Eric Helms
Yeah, no. And then in the influencer, they either haven't thought through it or they're explicitly not interesting and interested in educating people. Because education is not just providing science communication. And education is teaching people about science, which includes the process and how to come to better conclusions and know things more robustly and be progressively less wrong. But influencing, even if it is science based and it is accurate information, is just trying to get more people onto a certain belief set which someone else will then try to get them onto another belief set. And I think that is unfortunately what the vast majority of even science based information is on social media and why many people feel like they're constantly push and pulled. And you will see appeals to science to make someone a carnivore or a vegan in the same day. If you just go out into the wild, wild world. And sometimes they might even have PubMed IDs. Less so in the carnivore situation. But my point stands for various topics. Yeah.
- [75:00]
Eric Trexler
All right, Helms, I think, well, that probably wraps it up for tonight. Anything you want to say to the good people before we sign off?
- [75:06]
Eric Helms
If you've been struggling to hold on in this whirlwind of information, I can't help you. But if you've been struggling to hold on to barbells, I would highly advise you head over to elitefts.com and use our code MRR10, that is Mass Research Review 10 for a 10% discount that you can get on lifting apparel, equipment and bands and all kinds of fun stuff. That you can get at our good friends at EliteFTS. And the reason that code is MRR10 for Mass Research Review 10 is that this podcast is Iron Culture presented or powered by Mass. I kind of like that. I'm going to start calling that the way the way it is from now on. And if you want to dig into a lot of the writings that myself and Dr. Trexler have on protein, I think we're the. I guess we're the protein corner for the most part. I'm not that Zertos and Lauren have never written about it or our prior partners, but Definitely check out massresearchreview.com we got a new issue that has probably just dropped. If you're listening to When I think this episode will come out. And we got more on the way and a tremendous amount in our back catalog of over 1000 pieces of content. So it's never the wrong time to join Mass and now is not never. It is some time. So check out Mass Research Review.
- [76:24]
Eric Trexler
Absolutely. And the final thing I want to say, I know we said it many times before, it's getting gratuitous at this point, but I just want to reiterate, I really have a tremendous amount of respect for Stu. Stu Phillips. We kind of kicked this off by talking about a post he made that we nitpicked and quibbled about. But his contributions to the protein literature, these meta regressions wouldn't be filled in without a lot of the work that he's done. So his contributions to the protein literature are immense. And so I already had a lot of respect going into the conversation, but the way the conversation went and hand up, acknowledging my particular flavor of not neurotypical is such that when it comes to talking science, I tend to get very direct about the data in ways that have almost no smoothness whatsoever, no tact of any kind. And I was just immensely impressed with the way that the conversation went in him just as we talk through the data. Yeah, just, you know, respect going into the conversation walked out of it with even more so I really appreciate him for the. The dialogue and just kind of talk and shop with us and reaching that consensus because I thought it was really productive. All right. Helms.
- [77:43]
Eric Helms
I ended up playing good cop mainly as a, you know, a necessity rather than a plan.
- [77:50]
Eric Trexler
Yeah, I think you preemptively hedged because, you know, it's not that I play bad cop, it's that I play oblivious cop who gets so caught up in the data that I have no regard for just like the basic tenets of kind communication. You know, I just. I'm just very direct about it in ways that lack all social grace intact.
- [78:10]
Eric Helms
I'm here for it. You know, I. And I actually think there's enough people like you in science that it probably wasn't too abrasive, you know? Yeah.
- [78:20]
Eric Trexler
Yeah. I. I do believe if you. If you released scientists into the wild, that the disproportionately large portion of people like me who, when they're talking in a very targeted way about science, lose all social norms of communication, I think society would collapse. It couldn't stick together. At least any polite society would fall apart almost immediately.
- [78:42]
Eric Helms
Yeah. I don't think you're necessarily wrong. Yeah.
- [78:45]
Eric Trexler
All right. But the good news is it hasn't happened yet. And so we'll keep our fingers crossed and think that in seven days we'll still have a functioning society. So, everyone, thank you so much for joining us in this episode of Iron Culture. And we will be back in a week with yet another episode.