In this episode, my guest is Dr. Layne Norton, Ph.D., one of the world’s top experts in nutrition and training for physical fitness. We discuss how to evaluate scientific evidence and the validity of different practices aimed at achieving fat loss, muscle strength and hypertrophy, microbiome health, vitality, and longevity. We explore many hotly debated topics, including fasting, seed oils, saturated fats, sugar, red meat, artificial/low-calorie sweeteners, and GLP-1 agonists (e.g., Ozempic). Additionally, we delve into the timing of protein and carbohydrate intake relative to fasting and exercise, fat loss and sleep, and the benefits of dietary protein and fiber on overall health. We also discuss how to accelerate hypertrophy and fat loss, improve strength, whether we need to train to “failure,” how to enhance exercise recovery, and how to manage pain. We cover training before versus after age 50, whether metabolism changes with age, and the connection between muscle health and longevity. We also address why certain behaviors and supplements might work for some people but not others. Listeners to this episode will benefit greatly from Layne’s science-based expertise on a wide range of topics, including health, nutrition, and fitness. Access the full show notes for this episode at hubermanlab.com. Thank you to our sponsors AG1: https://drinkag1.com/huberman Mateina: https://drinkmateina.com/huberman Eight Sleep: https://www.eightsleep.com/huberman Maui Nui: https://mauinuivenison.com/huberman LMNT: https://drinklmnt.com/huberman Timestamps 00:00:00 Dr. Layne Norton 00:01:49 Sponsors: Mateina, Eight Sleep, Maui Nui 00:06:39 Science-Based Evidence, Mechanism vs. Outcome 00:14:31 Meta-analysis, Methods, Evidence Quality 00:22:45 Evidence Hierarchy, Randomized Controlled Trials, Cohort Data 00:33:53 Sponsor: AG1 00:35:05 “Don’t Turn Your Brain Off”; Protein Synthesis 00:42:01 Protein Synthesis, Refractory Response; Resistance Training 00:46:05 Protein Intake, Intermittent Fasting & Training 00:54:52 Tool: Total Protein Intake, Distribution & Protein Synthesis 01:00:25 Muscle Quality, Protein Remodeling, Muscle Growth 01:05:34 Sponsor: LMNT 01:06:46 Early vs. Late Time-Restricted Eating; Fasting Blood Glucose & HbA1c 01:10:30 Carbohydrate Timing, Individual Response, Placebo; Tool: Tracking Diet 01:19:50 “The Norton Method”; Tool: Consistency 01:25:16 Resistance & Cardiovascular Training; Competition; Immune System & Rest 01:33:50 Mind & Body Effects, Stress; Belief Effects 01:41:30 Training to Failure, Reps in Reserve, Hypertrophy & Strength Training 01:50:24 Fatigue & Training to Failure, Speed, Strength Training 01:59:06 Tool: Training After 50, Consistency 02:09:12 Fat Cells, Diabetes, Exercise 02:16:50 Metabolism & Age-Related Changes?, Appetite 02:23:17 Ozempic, Mounjaro, GLP-1 Agonists, Lean Mass, “Food Noise” 02:33:42 GLP-1 Agonists, Judgement & Obesity 02:40:19 Sugar, Excess Calories, Body Weight 02:49:16 Satiety, Sugar & Calorie Budget 02:54:56 Tool: Individualization, Context & Diet Psychology 02:57:22 Seed Oils, Butter, Olive Oil 03:06:56 Red Meat, Carcinogenic?; Simple Diet; Fiber Benefits 03:13:43 Saturated Fat, Cholesterol; Seed Oils 03:18:41 Artificial & Low-Calorie Sweeteners, Insulin, Appetite 03:29:06 Artificial & Low-Calorie Sweeteners, Gut Microbiome; Cancer 03:37:58 Tools: Training Recovery, Glycogen Replenishment; Stress & Activity 03:45:56 Collagen Supplementation, Skin & Nails, Whey Protein 03:57:00 Evidence-Based Approach 04:01:41 Zero-Cost Support, YouTube, Spotify & Apple Follow & Reviews, YouTube Feedback, Protocols Book, Social Media, Neural Network Newsletter Disclaimer & Disclosures
Welcome to the Huberman Lab Podcast, where we discuss science and science-based tools for everyday life. I'm Andrew Huberman, and I'm a professor of neurobiology and ophthalmology at Stanford School of Medicine. My guest today is Dr. Lane Norton. Dr. Lane Norton did his training in biochemistry and nutritional sciences, and is one of the world's foremost experts in exercise and nutrition.
He is also an expert in the topic of supplementation and other tools to augment health. Today, we discuss a large number of very important topics in these categories, and we start the conversation by establishing what Dr. Norton's thresholds are for what he accepts as evidence, in particular, actionable evidence.
So what follows is a description of what Dr. Norton really believes is worth paying attention to versus what he believes is worth ignoring in the realms of nutrition, training, and supplementation.
So you can be certain that as we start to go through the topics of sugar, GLP-1 agonists, things like Ozempic, artificial sweeteners, whether you should train to failure or not during your resistance training sessions, how much volume of training you need to do, cardiovascular training and its different forms in terms of how they benefit health span and lifespan and body composition, protein and its different sources, and on and on.
Indeed, we cover many topics in this episode. you can be sure that all of the information you hear from Dr. Norton is being filtered through that extremely stringent filter that Dr. Norton is so well-known for.
And thus, by the end of today's episode, you will be armed not only with the latest information on nutrition, training, and supplementation, but you'll also be armed with your own filter to determine what sorts of health protocols are actionable for you. Before we begin, I'd like to emphasize that this podcast is separate from my teaching and research roles at Stanford.
It is, however, part of my desire and effort to bring zero cost to consumer information about science and science-related tools to the general public. In keeping with that theme, I'd like to thank the sponsors of today's podcast. Our first sponsor is Matina. Matina makes loose leaf and ready-to-drink yerba mate.
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In particular, I love the taste of Matina's canned zero-sugar cold brew yerba mate, which has a slight taste of lemon, and I personally helped develop that drink. I drink two cans of Matina yerba mate cold brew in the morning, and I often drink a third can in the early afternoon. If you'd like to try Matina, you can go to drinkmatina.com slash huberman.
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Again, that's mauinuivenison.com slash Huberman. And now for my discussion with Dr. Lane Norton. Dr. Lane Norton, welcome back. Thanks for having me back. Before we jump in,
I want to get your stance on what constitutes evidence, because I think a big reason why you are considered one of the, if not the most trusted person in the realm of nutrition and training is that you set a very high bar for what you consider science-based fact that motivates action. So to just kind of break this down based on my read of the landscape online,
It seems that there's a group of people, I don't know what to call them, purists or something, who, unless there's a randomized controlled trial, so that means in humans, or several, that points in a particular direction, they are very unlikely to adopt a new practice, say removing a given food or nutrient, adding a given food or nutrient, training a certain way, not training a certain way.
Okay, that's one group. Then there are the people who, if they are told something could be of value, they hear it's worked very well for somebody, maybe they see some before and afters, and it gets mapped to a mechanism
that exists in humans and animals, like, oh, there's this molecule, and if this molecule increases, X, Y, and Z happens, and training in this way or eating this way increases that molecule, for instance, but no randomized control trial, then they're willing to try it or adopt it. And then there's a third, probably a fourth category as well, where people say, They don't trust science anyway.
Science is flawed or the controls required to design a really good experiment are so constrained that they don't mimic the real world well enough. And so they're really just interested in what works. So they look to people that seem to have achieved the results they want. feel free to add another group, but which group would you consider yourself in personally?
And then where does your evidence that you put out online and today come from? And I already know the answer to the last question, but I think it was important to kind of spell out the landscape.
So everything you just mentioned would fall into the category of evidence. Everything that we can observe is evidence. But I think what people really struggle with is the idea of different levels of quality of evidence. And if I had to put myself into a group, I have definitely been on the side of, well, there's a case study in this journal and we're going to try that now because it must work.
And or, you know, my friend tried this and they said it worked, so I'm going to try it. And then I've also gone to the group of, well, there's no human randomized control trial, so I don't believe it. And I think now, you know, I'm 42 now and I've been doing this for two decades. I think where I'd fall into is it really depends on how the individual is talking about the evidence.
OK, so as you can probably imagine, I get sent a lot of stuff for people to like, oh, debunk this. And a lot of times people will send me things and I'll go, hey, this person said this is their opinion. That's fine. Like I may disagree with their opinion, but I'm not going to like rake them over the coals for them saying this is an opinion or this is my personal experience. That's evidence.
It's low quality evidence, but it is evidence. I think I kind of fall in a line of I ideally want to see human randomized control trials, but. There's also, as you mentioned, practical limitations with how things are implemented. And I think one of the things that gave me a very unique perspective was the fact that I was doing my PhD in nutrition after I did a bachelor's in biochemistry.
So I had that mechanistic understanding. And then I had an absolutely wonderful PhD advisor, Don Lehman, who just – shout out to him – got a Lifetime Achievement Award by the American Society of Nutrition 20 years too late. But he was just incredible at being able to understand the small things – But how they impacted the big things and what it looked like overall.
It's like a conductor looking at a symphony, right? And understanding how the trumpet sounds affects everything else, but then not getting so tied up in that that he can't hear all the music, right? And he was so good at that and was so good at getting me to think that way.
And so I think where people out in the landscape trying to dissimilate this really struggle is they don't really know, well, this person decided to study and they equate that as evidence that's equal with any other evidence, right? And as a researcher, you know, not all evidence is created equal. Not all journal articles are created equal.
And I mean, honestly, people who don't have a research background, it's hard to unpack this stuff. So What I would say is you have to be very careful with people who cite studies. And one of the things I'll say, too, is there's nothing more dangerous than somebody who's read a biochemistry book because they're going to see pathway, biochemical pathway. There must be an outcome.
So outcomes are what we really care about at the end of the day. And when I say outcomes, gaining muscle mass, losing fat mass, Risk of cardiovascular disease, insulin sensitivity, cancer. But these are hard outcomes, right? And those outcomes are the summation of dozens, if not hundreds, if not thousands of biochemical pathways all summing up to an outcome.
And just because something has a biochemical pathway doesn't mean it will create an outcome. But if there's an outcome, there's absolutely a mechanism to explain it. Now, let me give you an example of why this stuff can be so complicated and why it's so easy for people to, if you want to create a narrative, you can always find a study to create a narrative.
Aspirin, we would agree, is an anticoagulant. There's a reason they give it to patients who are at risk for heart disease or a heart attack. It's because it reduces blood clots, reduces coagulation. It also activates procoagulant pathways. But the overall outcome is anticoagulation.
But if I wanted to create a narrative that aspirin was bad for blood clots, I could say, well, look at these biochemical pathways it activates. And you see this, like, for example, I could create a narrative that smoking is not bad for you, okay? I remember reading a meta-analysis of the effect of smoking on the risk of adenocarcinoma, right?
And there's a forest plot with probably about 50 studies. And most of those studies are to the very far right of the line, which is increases risk. And I think the overall effect was like 300% or 400% increased risk of adenocarcinoma. But there were two studies that were to the left of the line, not by much, and it wasn't statistically significant.
But I could say, hey, look, I could cite these two studies, PMID. You know, they showed no increased risk of adenocarcinoma and actually might be slightly protective. And by the way, did you know that smoking decreases the risk of Parkinson's by 30 to 40 percent? And by the way, that's very consistent in literature.
Yeah, that's true.
So I can start creating this narrative that smoking, but we know smoking is not good for you. It's not good for you. It raises the risk of lung cancer, all different kinds of cancers, cardiovascular disease, massive increase in risk, right? But I could thread the needle of science using these cherry-picked studies. And so what I'll tell people is if I go into a topic, if I go into something,
What I'm looking for, highest quality of evidence, is first off, do we have some meta-analyses on this topic?
Do you want to just explain for the general listener what a meta-analysis is? Just in kind of top contour.
Absolutely. So a meta-analysis is basically we are trying to compile studies that ask similar questions and look at what is the overall effect? Do we have a consensus in the literature? And usually they're going to show some kind of forest plot of all these studies. And however far right or left of the center line is kind of giving you an idea of how powerful the effect was in that study.
And then you can see the confidence intervals in terms of how much variability there was. And then you can see the thickness of the dot on there, which shows how much it contributed to the overall study. analysis by usually how many subjects were in it.
Right. One study with 10 subjects would have a very small doc compared to a subject with 500 subjects.
Exactly. And so you're trying to... Now, you can do a bad meta-analysis based on inclusion criteria, you know, and that's where it's important to look at. But let me give you an example of a meta-analysis I cite pretty frequently. The inclusion criteria is very important to make sure that you answer the question that you want to answer. And I say this when you're reading scientific studies.
I'm like, listen... Just because there's a headline in even a paper, just because the conclusion says something, that is the author's opinion. You need to check to see, did they actually test what they're talking about? And are the tests they use valid? So this meta-analysis was looking at lower carb diets versus higher carb diets or low fat diets.
And the inclusion criteria, this was done by Kevin Hall of the NIH back in 2017, I want to say. And I thought he did a great job at the inclusion criteria, which was we're only going to include controlled feeding trials where the food is provided to participants because obviously we know the limitations of, you know, free living studies with nutrition.
Right. Self-report. People sneak. People forget.
We are going to make sure that these studies equated calories for the reasons we talk about. You got to compare apples to apples. Right. So a lot of studies will come out saying fasting produce more fat loss. Low carb, but then they didn't control calories and it's very likely these people just ate less.
So they controlled calories and they controlled protein, which is also important because protein changes the composition of weight loss. Protein has a thermic effect. Protein increases lean mass retention. So that can change how much fat you lose. And I think they also had a requirement of like a minimum of four weeks, right?
And the outcome was looking at changes in fat mass, not fat oxidation, not energy expenditure. It actually looked at the outcome that they cared about. And they They showed no difference, right? So I thought, well, that's a very well done meta-analysis because the inclusion criteria make a lot of sense for the question that they want to answer, which is not, is one diet easier to stick to?
Not, is it more practical? The question was mechanically important. do these diets produce differences when we're comparing apples to apples in actual fat loss? And the answer was no, right? And then when you look at the other meta-analyses that have been done, they tend to kind of support that, right?
So the first thing I'm going to look at is, all right, these meta-analyses tend to be looked at as kind of the highest form of evidence, right? Because you're compiling a bunch of different studies, which, listen, we know there are bad studies that get done. I think the amount of studies that get like just straight up faked is probably much lower than people think. One hopes.
But yeah, I would agree. I think that people make errors. I do think that a lot of quote unquote bad papers, or let's just say false conclusions arise from elimination of data that did not fit the person's desired outcome. And the reason I say that is I think it's impossible to control for.
So you've got the student or postdoc doing the experiment, the results don't come out the way they would have preferred. And then they're, you know, let's just say I've observed before, never in my laboratory, fortunately, but
cases where people come up with reasons why that particular experiment wasn't valid because, you know, the mice were initially sick or the drug, the lot of drug that they used wasn't, it was heading towards expiration. They come up with reasons to exclude rather than outright data fabrication where people literally create results that aren't there.
And, you know, there are a number of different examples throughout history that where people have done that, but I like to think that those are more recent.
I think that's probably pretty small. My experience is the same as you. I didn't see much of that or I never saw it observed.
Usually end up reading about that in the form of retractions in journals that come out nowadays more close to the publications because of AI's ability to scan images and things of that sort.
Yeah, I think, you know, usually if I see a paper and the conclusion, like, just straight up, I go, oh, I don't know about that. When I go in and I read the methods and I read how they analyzed it and I read how they measured things, 99% of the time I walk away and go, okay, I'm not surprised they found what they found, right? Because, again...
A lot of, and this does happen and it shouldn't, but a lot of studies are set up to kind of find what people want to find. You can bias things in a certain way.
Well, and what nobody talks about where it's not discussed enough is that a lot of times the way the paper is written poses a question after the results are in. I mean, and this is a really a not correct way to do science. I mean, in clinical trials... one has to wager a hypothesis from the outset. And you go test that hypothesis. You're not asking a question.
And you have to say what you're going to measure.
Right, exactly. Whereas in more typical laboratory science, people will design an experiment. They have hypotheses. But then depending on how the experiments work out or don't work out, oftentimes they'll change the question, they'll modify the hypotheses. And one would, as a reader, as a journal, as a reviewer, one will never know.
And so that's a slight of hand that is, I would say, unfortunately, is very common in better science.
But I will say like there's very rarely do I say this was a bad study. Often what I'll say is, you know, I don't agree with their conclusion based on their data and their design, but the data is the data, you know. I was just very fortunate, again, to my PhD advisor. I have so much gratitude because he just right away was like, hey, if we're wrong about something, that's fine, you know.
And I'll give you an example of how results can seem to conflict, but, you know, how things are designed. We actually wanted to test, does protein quality make a difference? And we wanted to look at it at like, not low, but like just kind of like RDA levels of protein. And we saw that protein quality did make a difference at those levels of protein.
But if you look at experiments where people are feeding like high levels of protein, like 1.6 to 2 grams per kilogram of body weight, you don't really see much difference in lean mass or protein synthesis with looking at different protein sources. Well, that's because it's much more regulatory on a low end because you're closer to those thresholds that trigger that signaling.
And so we wanted to show at that level That it made a difference. But then we also acknowledge, okay, at this level, it probably doesn't make as much of a difference. But people can read those things and say, well, I don't believe studies because they're conflicting.
But no, when you read how it was designed, I can easily say, like, I remember there was a, somebody sent me a study and said, well, how does this fit with your data, which... They were comparing rice versus whey protein and found that both stimulated protein synthesis to the same degree. And I said, well, they used 40 grams of protein.
Like if you get protein high enough, you can max out protein synthesis regardless of the form of protein you're using. And so that's just like one of those examples, right? So when I'm looking through this stuff, I'm looking at, OK, does there seem to be a consensus in the data? And then is it like in these meta-analyses, does the inclusion criteria make sense?
And then if there's no real agreement amongst the meta-analyses, then I'm looking at, OK, what do the most tightly controlled studies show, like in the randomized control trials? And then I'm kind of like basing opinion off that. But, you know, the hierarchy of evidence, the pyramid, you got meta-analysis, systematic reviews, randomized control trials.
You have cohort data, epidemiology, and then animal studies tend to get kind of lumped in together. And then you got like case studies and so on and so forth, right? And so all that stuff is valid. It's all valid. I think where I spend a lot of time on social media is, for example, I'll give you a great example.
Someone saying, well, you don't want to eat cruciferous vegetables because they have isocyanthanates in them, which can bind to iodine. And that is going to impair your thyroid function, lower your metabolic rate and cause you weight gain. And so. That's a pathway. That's a mechanism. Is it possible? I suppose it is possible, right? That pathway does exist.
Iodine is important for thyroid function. Isocyanthanates do bind to iodine. You can take any food, even organic food, and you can find a compound in it. That if you fed it in a high dose, it would have weird effects, right? And so the question is not if you eat something, are there compounds in it that maybe activate negative biochemical pathways? The question is what is the overall outcome?
And so when these pathways are promoted versus let's see if we actually have randomized control trials in humans. that measure what we actually care about. And so we do have, like in that particular case, we have randomized control trials looking at, okay, cruciferous vegetable intake and thyroid function. And there's no difference in the outcome.
And so what that says, and then no difference in BMR. And then actually people who eat more cruciferous vegetables actually tend to be a little bit leaner, but that could be a little bit healthy user bias. And they'd probably just eat less calories because they're more satiated, but it's certainly not going the opposite direction. Right. And so,
The point is, again, if an outcome exists, there is absolutely a mechanism to explain it. But just because a mechanism exists does not mean you're going to produce an outcome. And I got exposed to this very early because I cut my teeth on the bodybuilding message boards back in the day where it was a bunch of nerds arguing with each other, mostly who had no background arguing.
But there were some actual like sports scientists and professors who would get on those every once in a while. This was before social media existed. And I remember I was in biochemistry class. This is 2003. And they're talking about how caffeine inhibits glycogen phosphorylase, which is a mechanism. And it exists. Caffeine inhibits glycogen phosphorylase.
And so I made this post where I – on the forums and I said, well, we should be having caffeine after a workout then because it will help with glycogen resynthesis because it will keep glycogen phosphorylase from breaking down glycogen. And somebody came in and said, you're really like zooming in on a blade of grass instead of zooming out and looking at the forest, right?
And biochemists – I was guilty of this and biochemists by trade, we get very focused on pathways. But if you think about what caffeine does overall, activates the sympathetic nervous system, its function is to, like, you're liberating fuel. And some people, when they take caffeine, actually have a rise in blood glucose. So that is the outcome.
is actually counter to what that biochemical pathway is. And so we've got to be really careful with how we promote these biochemical pathways. I mean, I did a really funny post on Twitter where myself and Joseph Sundell, I'm not sure if you're familiar with him. He's a cancer biologist.
Great guy.
And we were joking back and forth. I said, you know what? I bet I could like come up with a pathway to get people to eat poop. Like I can make a compelling argument for just eating poop. And then he goes, he's like, I'll take that bet. I'm like, okay, let's give it a shot. So I'm like, what is some of the most common compounds in human fecal matter?
And one of them is butyrate, right, which is a short-chain fatty acid produced by fermentation. Butyrate, so I did this post where I'm like, here's why you should eat poop to lose fat. Butyrate increases fat oxidation. I think it activates brown fat, increases insulin sensitivity, decreases inflammation. It's been shown to actually ameliorate the development of obesity in studies.
And so I had all these PubMed ideas. Now, what I didn't tell people was those are all mostly in rodents, right? And it's giving an amount of butyrate that you'd need to eat about 50 to 100 pounds of fecal matter a day in order to get, right?
It sounded like a worse and worse idea by the moment.
But that is very similar to a lot of the content that is out there, which is find isolated compound, scare people or promote it to be the best thing ever, and then link it to an outcome. And then sometimes you can tie in epidemiology with it as well to support whatever you want.
But again, like I'm not saying – I do things in my training and my nutrition that don't have randomized control trials to support, right? They don't really have anything to support. It's just – it's how I've kind of fallen into doing things. So that's okay. But what I wouldn't do is come out and say, what I do is the best thing ever.
And here's why, especially if there was human randomized control trials to the counter, that is the biggest thing, right? If we have human randomized control trials and they're going the opposite direction of a case study or an observation, there's a reason human randomized control trials, I scream about them all the time and why they're considered the gold standard of evidence.
When we look at cohort data, You're just observing people. There's no intervention.
Maybe explain what cohort data are. Comparing two groups.
Sure. So cohort data, you're comparing groups, but you're not having an intervention. So you're tracking them over the course of however, what period of time. A lot of cohort studies like looking at cardiovascular disease, cancer.
These people decided to be vegan. These people decided to be, let's just say omnivores.
Those are some of the classic experiments, right?
And they weren't assigned to this experiment. They agreed to join the experiment. They've been eating this way for a while. Right. You ask them a bunch of questions.
And you look at, okay, over 10 years, over 20 years, who gets whatever more often or less often, right? And then we try to figure out and calculate, okay, what's the effect and is this real? The problem is you have a lot of bias with those sorts of studies, meaning people don't do single habits. They don't isolate habits.
I actually put up a reel the other day from my appearance on Stephen Bartlett's podcast where he said, if I want to fix my diet, I go to the gym. Because a lot of people do that. If they're training in the gym, they don't want to waste their effort by having a subpar diet. Now, in reality, eating a healthy diet is more important if you're not going to the gym, right?
Because at least you're getting something. But people do this habit coupling. And so it's really hard to disentangle those sorts of things. Now, the reason that human randomized control trials are important is if you're designing an experiment and you randomize What you are doing by randomly assigning people to groups, you're washing out that bias because.
you can assume that whatever inherent characteristics that might be coupled to whatever you're going to try are going to be randomly distributed and evenly distributed across the groups.
Therefore, we say human randomized control trials are kind of what's needed to establish causation because by randomizing, you can assume whatever differences are observed between the groups are due to your treatment and not due to random chance or data artifacts. Now,
Randomized control trials, especially in nutrition, have very strong limitations, which is you can't do a randomized control trial for 30 years. I mean, I think the longest randomized control trial I heard about in nutrition is like two years long, right? And even then, it's not going to be a very tightly controlled randomized control trial.
I mean, and if you're talking about like the tightest level of control, like a metabolic ward study, Four, six weeks, maybe, because you're keeping people in food jail. And I think where some of this confusion comes from is I think people think that there's just like this pool of people waiting around to be selected for experiments. Like, yes, I'm ready. I've been waiting here.
No, there are people like you, like me, like just the average person walking down the street who saw a flyer and goes, OK, I'll volunteer for that. And the more control you try to establish over their lives, the less likely they are to do it. And you probably got to pay them. You know, I don't know anybody who would do a metabolic ward study without getting paid for it.
I mean, you're basically giving up four or six weeks of your life to go do that. And so while I love human randomized control trials for some things, they're not always appropriate. For example, if you're trying to look at heart disease and you want to do a one year human randomized control trial, looking at, say, saturated fat, LDL, cholesterol, those sorts of things.
Well, how many people have heart attacks within one year after age 60? I mean, you're going to look for differences between really small numbers, right? And the problem with that is you have no idea about their diet 40 years leading up to that. And we know, based on now the Mendelian randomization trials, that the risk of LDL is more of like a lifetime exposure risk.
It's not just in this narrow sliver of time. And so I love human randomized control trials, but it's also, I try to tell people Never turn your brain off. Just because something gets published in a certain journal, just because a certain researcher said something, just because it was a certain design, it doesn't make it infallible, okay? Science is perfect. Science is perfect. Science is what is.
But it's done by humans, and humans are fallible, imperfect people with their own personal beliefs and biases that And that's why I look at consensus of data first because, yeah, you could – maybe some experiments got faked or maybe they had – but when it's done over – let's take something like creatine monohydrate, right? You have thousands of experiments done over decades of time.
in hundreds of different labs with many different funding sources in a bunch of different countries under a bunch of different conditions, it works, right? Like if you go to consensus and you type in, does creatine build muscle? It's like 92% yes, which is crazy.
Right, consuming five to 10 grams of creatine monohydrate per day is going to benefit strength and muscle mass and likely cognition to some extent. Oh, yeah, yeah. I'd like to take a quick break and acknowledge our sponsor, AG1. By now, many of you have heard me say that if I could take just one supplement, that supplement would be AG1.
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I realize I've been on a long diatribe. No, no.
I wouldn't even call it a diatribe. I think for those listening, this is pure gold because... Never before, certainly on this podcast or other podcasts, has anyone ever really spelled out how to discern differences in quality of evidence. And it's mostly a free world, most places, and people can do what they want, but I think they need to decide what their thresholds are for quality.
Yeah. And I think the one other thing I'll tell people is I saw this the other day. I saw somebody post. I think it was a comment on one of my posts. I actually commented back. They said, you know, I just I know I can trust you. And I just whatever you say, I know I can take it to the bank. And I said, I appreciate that. But I am a flawed human like anybody else. please don't turn your brain off.
And one of the things I've really tried to do now in this stage of my career is I want to teach people how to think. Because if I just give you the information and I'm giving you a fish, great. But I'd rather you understand how I came to these conclusions. You can see my logic and how it tracks. And then you can start applying it elsewhere. And one of the things I say to people is I'm like,
if you want a quick and dirty hack for knowing who to follow, try not to listen so much to exactly the information people say, but listen to how they say it. Okay. I was just telling you, I was on a podcast the other day where I said, you know, here's this study. I might butcher the details. And if I get the math wrong, if experts out there want to comment and correct me, please do that. Like,
That is a way of talking about something where you're saying, hey, I could get this wrong or hey, I might be uncertain. That's very different than saying, you know, just hard, pure, you know, real experts don't really talk like best, worst, always, never. Like they don't really use words like that.
And one of my favorite phrases that I tell people, it's actually from an economist named Thomas Sowell. He said there are no solutions. There are only tradeoffs. And for example, you know, there's data out there that if you lower saturated fat, it may lower your testosterone.
But there's also data out there that saturated fat raises LDO, which is an independent risk factor for cardiovascular disease. Okay. Well, there's tradeoffs there, right? Like what do you value more? I would argue that probably the decline in testosterone isn't really physiologically meaningful for most people. But again, there's not a good or bad. There's tradeoffs.
And I think when people get talking about biochemical pathways, one of the things I really try to hone in on is like, hey, there's not really good or bad biochemical pathways either. Like all these things exist for a reason. Like people, like one of the things popular is like, well, inflammation, inflammation.
I'm like, Hey, you know, like inflammation does some things that we really need to, like, you just don't want like no inflammation. Like it's actually important physiological process, right? Now you don't want it to run away for sure. And so again, I just give my PhD advisor a lot of credit of, he's like, know what you know, but always question yourself.
Everything, even the things we feel most fundamentally are true, because that is the job of a good scientist. I'll give you one more story and then we'll move to another thing. When I did my first experiment, well, actually, sorry, no, this has been like my 15th experiment because my first 14 blew. It didn't work.
A typical graduate career.
Yeah, yeah. And again, very patient man, very supportive. I honestly cannot give him enough credit. And if you look at the people that came out of that lab, a lot of studs. So I did an experiment looking at complete meal with whey protein ingestion and how long the duration of muscle protein synthesis was.
Because most people kind of measured at 60 or 90 minutes, like the snapshot postprandially for protein synthesis, looking for a peak. And we're like, is that really where the peak is? We don't know. We're basing this off of purified solutions. So let's do a duration experiment, right?
And my hypothesis was, well, however long leucine is elevated in the blood is going to be how long protein synthesis stays up. And when we got the data back on protein synthesis, protein synthesis had come up, peaked at 90 minutes and by three hours had come back down to baseline. And I went to run the plasma amino acids and I'm like, OK, well, this is what we're going to see.
And that's not what we saw. So plasma amino acids not only were still elevated, they were like maxed out or plateaued at the highest level they would be at three hours where protein synthesis was back to baseline. And so I said, OK, well, it's got to be mTOR signaling. mTOR signaling is going to be turning off. Or something's happening. Nope, mTOR signaling was still elevated, right?
And we saw this through phosphorylation of the binding protein for EBP1, which is a proxy for mTOR activation. And then I said, okay, well, maybe leucine isn't getting into the cell. Maybe that's why. So we looked at intracellular leucine, followed the exact path of plasma leucine. And so then I kept rerunning the plasma data over and over. I probably ran it five times. Right.
And Lehman finally calls me into his office one day and he goes, so where do we stand with this duration experiment? And I said, yeah, it's almost done. I just I got to run the data again because the plasma data has got to be wrong. And he I saw his like little eyebrow go up, you know, and he goes, why do you think that? Let me see your data. He goes, your standard air bars are good.
This looks to be relatively tight data. How's your technique? And I'm going through like how I – all the steps to analyze plasmido acids. It's not like CSI, by the way, everybody, you don't just like take a pipette, put something in a centrifuge and all of a sudden you get back data. There's many steps in here.
And so I showed him all that and he goes, you know, it sounds like you are trying to get the data to fit your conclusion. And what you need to do is change your conclusion to fit the data. And that one line. again, it just opened my whole world up to one. If I'm wrong, okay, cool. Like I care more about getting the right answer than being right. And that's why we were talking earlier.
I'm like, there's so much stuff that I just don't believe. I want to see 10, 20 studies before I go. Yeah. You know? And the other thing I'll tell people is, Hey, I don't plant my flag real strong very often. So when you see me do it, I'm not saying I'm not fallible, but if you see me do it, you probably should pay attention because I don't usually do that.
I love that description, but now my curiosity is peaked and you got to tell me. So if 90 minutes after ingesting protein, protein synthesis peaks and then it drops to baseline at three hours, but leucine, one of the key amino acids in mTOR, which is in the pathway of cellular growth and protein synthesis are still elevated at three hours. What is the conclusion that explains the discrepancy?
Yeah. So we actually looked for this for years. So a few things. There was some other studies that supported that. We called it a refractory response. Actually, we didn't name it that. There was another lab named it that. Basically, that protein synthesis was becoming refractory to the signal for protein synthesis. So just for real quick, I'm going to try and explain this easily.
So protein synthesis, you know, this sounds like probably a very abstract thing, but it's how you make your body makes more protein. And whether it's in skeletal muscle, whether it's in the liver, whatever, you have your DNA, which is your genetic code, right? And then that gets transcribed to an mRNA. By the way, I'm leaving out a lot of steps here, but just bear with me.
That mRNA gets translated by a ribosome into a polypeptide chain or a protein. So a ribosome is basically attaching to the mRNA and then based on the mRNA sequence is bringing in amino acids to match that sequence. So all the proteins in your body are coded for in your DNA, right? So when it comes to this process,
There's a complex called EIF4F, which acts as a scaffold for the ribosome to hook on to the mRNA. And EIF4F, the formation of it, is basically rate-limited by the association of two proteins called EIF4E and EIF4G. And EIF4E is bound by a binding protein, 4-EBP1.
And when you stimulate, when leucine stimulates mTOR, mTOR stimulates the phosphorylation of 4-ABP1, which makes it unavailable for binding with EIF4E. It can bind to EIF4G. that EIF4F complex can be made, brings the ribosome onto the mRNA, and now it can read, it can translate it. So there's a little cellular biology lesson for you.
Yeah, and if people didn't follow that, don't worry about it. What Lane's describing is that the presence of a bunch of molecules involved in protein synthesis is necessary but not sufficient for the protein synthesis. Right. A few other things have to happen. Correct. And apparently those other things are not happening after... 120 minutes.
So another lab called it the, the muscle full effect. Basically the idea is like once you've initiated that signal, uh, it kind of runs and then it's done, right? And just pounding more amino acids into the system is not going to further stimulate. In fact, there was a study done back in, I think it was 2001 by, I want to say by Rennie, another very well-known protein lab.
And they infused essential amino acids for six hours and looked at skeletal muscle protein synthesis. And they found it went up and then came back down by two hours and then never went back up, right? Good experiment. Yeah, very interesting. So- We looked at a bunch of different things.
The only thing we found that perhaps explained it a little bit, and I'm sure there's other labs that would argue with me on this. And again, this is in rat skeletal muscle, which, by the way, is a good model for human protein metabolism, but still. We looked at intracellular ATP levels and actually found that they were declining kind of in concert with the decline in muscle protein synthesis.
And muscle protein synthesis is an ATP-dependent process. But the process of protein turnover is energetically expensive. It's one of the reasons that protein has a higher thermic effect of food. And so our hypothesis was perhaps – By the effect of protein stimulating protein synthesis to start this machinery is energetically expensive enough that eventually you kind of run out of steam.
And so you have the signal there. But it just kind of ends, right? Now, there have been other experiments like Jordan Tromelin just published a paper a few months ago that got a bunch of feedback. It was 100 grams of protein after a resistance training exercise and saw that it was basically – like a lot more of it was used than we thought would be used.
Right, because for many decades, it has been – purported believed and propagated that the maximum amount of protein that you can utilize after a meal is 30 grams became the holy number. And this study essentially showed that more than 30 grams can be used not just as energy, but for the sake of protein synthesis in muscle. Correct.
Yeah.
And it, and how did that study land with you, given that it's one study without going into all the details that did that inspire you to change anything about your protein intake after training?
So what I tell people is I don't make big shifts in my opinions based on single studies.
Yeah, why does that not surprise me?
It shimmies me a little bit, right? And even before that study came out, what I had said is I think protein distribution matters, but I think it matters much, much less than total protein intake per day. Because all we need to do is look at some of these resistance training studies with intermittent fasting, right? where people are eating all their protein in an eight-hour window.
And theoretically, you would think they would get less muscle growth, especially based on this refractory data, because less time to stimulate. But at least in the studies out of Grant Tinsley's lab, I think there's two studies that were very well done where we don't see that. Now, important to point out, they trained during their feeding window and they had three.
They made sure they ate three high quality, high protein meals during that eight hour time. Right. So at least in that context, there was no difference in the amount of lean mass gained between intermittent fasting groups versus continuous feeding groups.
now. And in the continuous feeding groups that, uh, do you recall what duration they were eating their meals over? Was it probably 12 hours or so?
I don't recall specifically, but I don't recall an actual defined time.
I'd have to go back more than eight hours for sure. You know, I'm so glad we're landing here because my first, um, let's just call it sort of operational or actionable question, which came from, um,
you know, asking on social media for questions for you was many, many people, if not in the thousands, asked how to make sure that they're getting enough protein if they're doing something like intermittent fasting. And I myself fall into this category. I don't do it for any specific purpose.
This was long before Sachin Panda started doing his work on time-restricted feeding, aka intermittent fasting. But I don't tend to want to eat any food until about 11 a.m., Occasionally I wake up hungry like this morning and I had some eggs, I was particularly hungry, but I think that's representative of a lot of people. I want hydration and caffeine in the morning.
I want to train in the morning and then I want to eat pretty soon after I train. But what that means is that I'm eating during an eight to nine hour feeding window. And if I only manage two meals in there and a snack and I can only assimilate or excuse me, I can only put 30 grams of protein per meal toward protein synthesis.
We have to be careful not about using it for energy, but toward protein synthesis. Does that mean that I'm not going to hit my target of one gram of protein per pound of desired lean body mass? Because I'm 100 kilograms. I weigh about 220 pounds. I can easily eat 220 grams of protein in a nine hour period. Like give me three ribeyes. I'll eat all three. I love ribeye steak. Right.
But the question is, can I use that?
So I'm going to bring this back around to that particular experiment. So over time and when I left grad school, my position was that. It matters. Protein distribution matters. So I'll give you the straight down the line scientific answer and then I'll give you – if you inject me with truth serum, what I really think answer. And so we did an experiment, again, in rats.
We fed them completely same diets, same total calories, protein, carbs, fats. But – Uh, in one group, they got that pretty much evenly across three meals and the other group, 70% of their protein was coming at their last meal. And then the other two meals were like 15% protein, 15% of their daily protein. And 11 weeks, again, 11 weeks out of a rat's life, rodents live 18 to 24 months.
Um, that's a big chunk of their life, right? Yeah. And we did see about a 5% to 10% difference in the weights of the hind limbs in terms of muscle mass.
In what direction?
Favoring equal distribution, right? Now, again, hard to repeat that study in humans, right? And for the duration it's done. So I came out saying, you know what? That's actually less than I thought we were going to – I thought we were going to find bigger differences than that.
Because, I mean, if you're thinking about number of times you're stimulating protein synthesis, I mean one per day versus three per day, I mean shouldn't there be like a pretty significant difference there? And it was – I mean it reached the level of significance. But again, I thought the effect size was smaller than I thought. And so I kind of walked out saying, you know what?
Total protein intake is the most important thing per day. And then if you can distribute it relatively evenly, that's maybe the last 5% to 10%, right? And you've seen some human studies where it seems to matter. Most seem to show it doesn't really matter that much. Here's what I think. If you're measuring an outcome like lean mass, that doesn't change much in eight weeks, unfortunately.
It's very small differences. And so I think it's going to be hard to detect that. But what I'll tell people is if you're asking, can you build muscle intermittent fasting? Absolutely. Can you build a lot of muscle? Probably. If you are a bodybuilder, specific population, or if your goal is to be the most muscular, strongest human being you can possibly become,
I think you're probably better off not doing intermittent fasting just because those last that last five percent may make a big difference. And you're never going to be able to pick that out of a human randomized control trial in eight weeks. At least I don't think you will.
And so, again, I don't have any human data to really back that up, but just based on what I know about signaling and the effects we saw on animals, that's kind of my recommendation. But most people don't fall in that category. Most people are just worried about, hey, I want to look good, build a little bit of muscle. Intermittent fasting is a perfectly fine tool for doing that.
I will say, you know, obviously we haven't studied some of the more extreme forms of fasting in terms of building muscle, right? Like the 16-8 has been studied previously. But like I'm thinking of a study that was done without resistance training, alternate day fasting versus continuous kind of normal feeding.
One day no eating, next day eat.
Right. So they did.
Brutal. They did. Or at least for somebody like me. I can't think of anything worse. I'd rather fast for three days in a row and then eat for four days in a row simply because I know that by day two, it's probably going to get easier, not harder, but on, off, on. Fasting, eating has got to be just torturous.
So the way they did it was they did – the continuous group was getting 75 percent of their maintenance calories per day, so in a deficit. And then the alternate day group was doing 150 percent and then zero, right? So you're getting an average of 75. And they actually saw differences in lean mass at the end of that study, the continuous feeding group.
lost less lean mass than the alternate fasting group. So that's only one study and it didn't have resistance training. It's possible that resistance training could attenuate some of that stuff. But what I'll say is, you know, the more extreme forms of fasting probably aren't optimal for lean mass, right?
Also, can you imagine training on a day of complete fasting? Three hours after that, you're going to be dying. And then you can say, well, you could just train on the days when you eat. But then if you ever train legs hard, which I know you do, or if anyone does, and then the next day you're not going to eat anything. The day after training legs properly, my appetite's increased.
Yeah. So I think this is where the rubber kind of meets the road in terms of straight down the line. The randomized control trials say this, but I still do something a little bit different. Right. Because the randomized control trials say protein distribution doesn't really seem to matter. Right. But again, you inject me a true serum. I think it probably does matter a little bit. Right.
Now, does it matter as much as total protein? Absolutely not. That is by far the biggest lever. But again, if my context is I want to become the most muscular, strongest human being I can be, which I do because that's where I compete, I'm going to distribute my protein probably over four to five meals per day, right?
And so for you just personally, what time of day do you wake up and when is your first meal?
So, well, it's summer right now. So kids are off of school. So we're usually getting up around like seven thirty, eight o'clock in the morning. And my first meal is usually within an hour. And then I usually eat within an hour of going to bed and then I'll have two or three meals in between those. So usually I have about four meals a day.
Sometimes I'll have five if it's just a longer day or just how my timing kind of goes or whatever.
And does each one of your meals include approximately 30 plus grams of quality protein?
Yeah.
Some starchy carbohydrate, fibrous carbohydrate, and some fat?
I mean, sometimes they end up being like mostly protein or whatnot. But for the most part, there's a mix in each one. And usually around 50 grams of protein at a meal. Yeah. About 235 grams of protein a day. Some people would argue that, oh, that's more than you need. The research has shown that 1.6 grams per kg maxes out the response. Here's the thing.
And again, this is where like scientific experiments are big, blunt instruments. OK, they will tell you what not to do more often than they will tell you what to do. OK. When it comes to protein, my personal opinion, and this is just, I guess, a little bit of intuition based off of 20 years of studying this stuff.
is that I don't know if there's an actual amount of protein that maxes out the protein synthesis response. I would bet, if I was a betting man, that it's kind of an asymptote. You're familiar with, yeah.
So you're- Not everyone's watching. I just drew an asymptote. Right, yeah. An asymptote plot. But for those not watching, just think about a plot quickly rising very, very high and then essentially stays stable at the high level. Yeah. Maybe with a slight bit of taper.
Yeah, so it's easier to explain if it's going towards zero. So an asymptote might be, okay, you start out, you have 10, then five, then two and a half.
Okay, so you're running in the opposite direction.
Right.
Still asymptote going from high to low.
Right.
So asymptote can go from low to high, it can go from high to low.
Correct. So I'm trying to explain it because it makes more sense when people kind of go this way. you never reach zero, but it keeps getting incrementally closer on the other end. I don't think protein synthesis ever maxes out. I just think the increment of increase becomes so small that practically there's no difference and you wouldn't see a difference in outcome. Right.
And so I think that, you know, one, you know, there's debate over is at 1.6 grams per kg, 2.4 grams per kg. I, and there's even been a meta regression that showed up to 3.3 grams per kg had benefits and, I think a lot of this is with protein synthesis, you're looking for small differences between small numbers. It's not a very sensitive analysis, to be quite honest with you.
And, again, we would never be able to pick out those differences. And I'm thinking about there was a study by Stu Phillips who, if people don't know who Stu Phillips is, he's the best researcher going in protein metabolism right now. But one of the best. Sorry, I don't want to tick anybody off. And he did a study probably 15 years ago where they gave people different levels of egg protein.
And they looked at 5, 10, 20, and 40 grams of egg protein. And their conclusion was that 20 grams of egg protein maximized the protein synthesis response. But that's because straight down the line, if there's a p-value of more than .05, you can't say there's a difference, right? But if you looked at the absolute difference between 20 and 40 grams, I think it was like 11%.
And if you look at the graph, it almost looks like the start of an asymptote, right? Now, this was one study, wasn't a huge subject number. But that's kind of where my personal thoughts land on it, that kind of also support this, okay, 100 grams at a meal could still be utilized, is I'm not sure if there's a max out. I think there's a practical max out where you get to a point where –
hey, you're like slamming down 50 grams more protein for 0.0001% more protein synthesis. It doesn't make sense. But yeah, we'll never be able to – I doubt we'll be able to pick those numbers out in actual scientific experiments. And the other thing to keep in mind with this whole protein metabolism picture is we're really only talking about one side of this equation. So –
Net gain or loss of skeletal muscle mass is the balance between protein synthesis and protein degradation. And most of us protein researchers just kind of stick our fingers in our ears and go la, la, la, la when it comes to protein degradation because it's so incredibly hard to measure.
And so, yeah, like when we start to put all that stuff together, it's like now this picture gets really complicated. So what I tell people when it comes to that kind of stuff is listen. You could really get into the weeds on this stuff. The big rocks are about a gram per pound of body weight.
If you want to really, for all intents and purposes, max out the anabolic response, you're going to be fine.
One gram per pound of body weight, which is what Dr. Gabrielle Lyon also essentially recommended.
Right. I'm probably like, you know, real sticklers might be like, no, it's actually more like 0.7 or 0.8. And then it's, well, it's actually based on lean mass, which I agree with. But just for all intents and purposes, you could say...
you know, your body weight, ideal body weight, whatever it is, that number is, is going to be very sufficient for maxing out muscle building for the majority of people.
And we should probably point out, not just for muscle building, um, unless you disagree, uh,
and feel free to, of course, not that I need to tell you that, Dr. Gabrielle Lyon, when she was here, made a really key point, which is that ingesting sufficient quality protein each day isn't just about building muscle, even for folks that don't want to build muscle, and perhaps even particularly for women who assume that, you know,
building muscle can be a runaway process that maybe they're going to build too much muscle. That's a false assumption, of course, that ingesting one gram of protein per pound of body weight or ideal body weight is going to be beneficial because it's going to improve muscle quality, one's own muscle quality, the health of the muscular tissue.
And then she did an excellent job of relating the health of muscular tissue, skeletal muscle that is, to overall health and longevity. So I just raised that because I know that many people listening to this probably want to add a little bit of muscle here or there. Some perhaps want to keep the muscle they've got and lose fat. And some, of course, want to add a lot of muscle.
But it sounds like the recommendation is always the same. Since we need to eat sooner or later, one gram of quality protein per pound of lean body mass or current body weight or desired body weight, that's going to be a good starting place.
Yeah, for sure. And I, I think I would tend to agree with her, you know, the process, because when you eat protein, You're not just going to start laying down slabs of lean tissue just for meeting protein. There has to be a stimulus, which is resistance training. Or some people would argue you could stretch really hard and get the same thing, which there may be some evidence of that.
With weights and lift them in between stretches. No, I'm just kidding.
There actually are studies now where they like put people in like really kind of hardcore stretching for, you know, several minutes and they actually see hypertrophy with it. Yeah, very interesting. We could talk about those if you wanted. But the point is, either way, it's mechanical tension, right? So that's the stimulus to build muscle, to lay down lean tissue.
But the process of remodeling is probably beneficial for multiple reasons. So when you eat protein, like we said, synthesis goes up, degradation goes up, right? Because you're stimulating that process. You're stimulating protein turnover. One, that's relatively energetically expensive, all things being equal. So that's where the thermic effect of protein comes from.
Because people say, well, it's the urea cycle and this and that. Most of those ATPs, you get back in different phases of that cycle. Really, in my opinion, the thermic effect of protein is due to that kind of activation of this futile cycle of you're building more protein, but then you're also breaking down more protein. And so part of that is you are remodeling. You are making sure that
That protein is higher quality in that tissue by continuously breaking it down and building it back up. And so I would probably agree with that. And then again, even if you're in a resistance training program where you're not really building much more muscle anymore, the process of remodeling is probably good for you, you know.
And I would just say try to ally some of these concerns from people who are concerned about gaining too much muscle. So I have been lifting really hard consistently for 25 years. I am very comfortable with saying I train harder than almost anybody else you can possibly imagine. And anybody who has trained around me will back that up, back me up in the comments. I train very hard.
And in a shirt, I look like an athletic guy who lifts. I don't look like a monster, you know, like you might see pictures of me when I was a bodybuilding show and like very, very lean. And that looks, you know, over the top. But for the most part, I just look kind of athletic and I spent my entire adult life trying to get too big. Right.
So for most people, unless you're on performance enhancing drugs or you just have incredible genetics, that's not going to happen. And if it starts to happen, just back off on your lifting. Easy fix. So yeah, I think most people's concern with that is a little bit misplaced.
And the other thing I'll tell people is like, hey, some of these fitness, especially for women, a lot of these fitness models you follow, they show you certain workouts they do. They built that physique by lifting weights, right? And you're thinking that's a toned feature. Now, that person is actually pretty muscular, right? And so, again, especially for women, there are exceptions.
Some women have very great genetics for building muscle. They usually wind up in track and field, that sort of thing. But it's very hard to get too muscular for a woman. And what I'll say is like, you know, typically muscle looks good and fat is what makes you kind of look bulky, you know?
So again, I don't want to paint with too broad of a brush, but I would say that you don't really have too much to worry about when it comes to, to getting too muscular.
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since we're sort of in this realm of protein, maybe we build out from there, because a lot of questions related to something akin to the following. So, okay, so somebody strives to get one gram of quality protein per pound of body weight per day. And I realized that whether somebody follows a pseudo intermittent fasting thing where their first meal does a
you know, around 11 and they end, you know, and they finish up eating around 8 PM or a more traditional eating schedule really is just the addition of one more meal, like in the morning. It's like whether or not you eat breakfast. And of course, some people shift it the other way. They start with breakfast and they don't eat dinner, but
Um, I would argue that in order to, if you have kids or a social life of any kind, the, the most people can deal with, um, sitting across the table with someone just having a cup of coffee for breakfast, but it's sort of awkward. You limit yourself a lot in life. If you, if you can't eat dinner with other people, I don't know, at least.
And that's, and that's again, where the rubber meets the road with what is practically doable. Cause there've been some of these like circadian rhythm studies that suggest, well, maybe early time restricted feeding is better than late time restricted feeding. Um,
The more high-quality, more rigorously controlled, randomized control trials that are coming out now seems to show that it doesn't really make a big difference. And some of the – again, the measurements you use matter, right? So there was actually a very recent study where they looked at 12 weeks. They provided all the food to participants, equal in protein, calories, the whole deal.
The only difference was one group was eating 80% of their calories before 1 p.m., and they had an eight-hour feeding window in total. The other group had a 12-hour feeding window and were eating over 50% of their calories after 5 p.m., I want to say.
And so really like based on some of the chrononutrition stuff we've seen from some of the lesser well-controlled trials, they were expecting to see differences in like glucose metabolism and whatnot. And they just didn't really see a difference in anything. And the only – I think the only thing they saw a little bit of a difference was in fasting blood glucose. And here's what I tell people.
When you see a difference in fasting blood glucose but not HbA1c, you're looking at a transient difference. And what I mean by that is HbA1c is such a great measurement because it's an area on hemoglobin that can be glycosylated. And so that is very dependent on what is your overall concentration of glucose in the blood over a 24-hour period of time.
because it's exposed the entire time it's in your bloodstream. So whether you're getting glucose spikes at meals or you have higher fasting blood glucose, it's going to be very reflective of the overall 24-hour area under the curve, right? So why do some of these studies see a little bit better improvement in lowering fasting blood glucose, whereas HbA1c doesn't show up? Well, think about it.
If somebody has early time-restricted feeding and they finish most of their food intake before 1 p.m., They have an extra like six, seven, eight hours that they're not hardly eating anything. It doesn't surprise me that the next morning, because they've technically fasted for longer, you have a lower blood glucose. Now, I can't really back this up straight up because nobody's ever measured it.
But that, I think, is a logical explanation while you see some of this stuff. And that's why I tell people the measurement you take really matters. I think fasting blood glucose is a useful measurement. But I put much more value on something like HOMA-IR, euglycemic clamp, or – HBA1C. So anyways, I think the early versus late time restricted kind of doesn't matter too much.
Great. You answered a future question right there. You're telepathic. That's what you didn't know about them. The So the scenario here is whether or not meals are distributed evenly through the awake day or stacked a little bit more toward the morning or stacked a little bit more toward the evening. If somebody gets that one gram of quality protein per pound of body weight,
then they need to make up the rest of their calories with other stuff. And we have, broadly speaking, starches, fibrous fruits and vegetables, starches, and of course fats. And weight gain and weight loss, I think we both would agree, or weight maintenance, is going to largely be dictated at this point by whether or not you consume more calories than you burn or not.