Welcome to the Physiology Friday newsletter.

Details about the sponsors of this newsletter and deals on products I love, including Ketone-IQ, Create creatine, Equip Foods, and ProBio Nutrition can be found at the end of the post.

This week, I’m sharing the video of my recent appearance on the Down to Health podcast, where I talked about VO2 max, wearables, cardio myths, and other topics related to training, fitness, and longevity.

This was by far one of the most fun podcasts I’ve ever been a part of, and I think you’ll love it. Be sure to give the guys at DTH a subscribe if you enjoy their show!

Ketone-IQ is high-performance energy in a bottle. I use it for post-exercise recovery along with enhancing focus, mood, and cognition. Take 30% off your order.

Create is the first “modern creatine” brand. They sell a wide range of creatine monohydrate gummies, and just launched a new creatine + electrolyte mix product plus travel-size packets of creatine gummies. They’re giving my audience 20% off their order.

ProBio Nutrition—the all-in-one supplement that I use every single day—is offering 20% off. My preference is the tangy orange flavor, but they also sell an unflavored “smoothie booster” that’s great in a shake, smoothie, or juice.

• Equip Foods makes some of the cleanest, best-tasting protein products around. I am absolutely obsessed with their Prime grass-fed protein bars (the peanut butter ones are to die for, but they also just released a new chocolate peanut butter flavor).

Greetings!

Welcome to the Physiology Friday newsletter.

Details about the sponsors of this newsletter and deals on products I love, including Ketone-IQ, Create creatine, Equip Foods, and ProBio Nutrition can be found at the end of the post.

We like to think of exercise as falling neatly into one of two buckets: aerobic (“with oxygen”) or anaerobic (“without oxygen”).

Sprinting is anaerobic. Endurance exercise is aerobic. Nice and simple.

The problem is that the body is not nearly that tidy… it doesn’t flip from one energy system to another like a switch. It relies on overlapping pathways from the very first seconds of exercise, and what changes is the relative contribution of each pathway as the effort goes on.

I think this is one of those areas where exercise science has suffered a little from its own terminology. The labels “aerobic” and “anaerobic” are useful, but they can also be misleading when they get turned into hard categories instead of broad descriptions. And it’s really affected the way we (incorrectly) talk about exercise.

A background on energy systems

Muscle contraction always runs on ATP. That’s the immediate currency.

But ATP stores inside our muscles are tiny, so the body has to regenerate ATP constantly during exercise. It does that through three main bioenergetic pathways: the phosphagen system, glycolysis, and oxidative phosphorylation.

The key point is that these systems are always interacting. One may predominate, but they are not taking turns.

This is a concept underscored and (updated) in a new review paper.1

There’s a nice bit of historical context in the review.

A lot of this conversation traces back more than a century to the earliest studies measuring oxygen uptake during and after exercise. That work led to the concept of oxygen deficit, which is basically the gap between the oxygen an exercise bout would theoretically require and the oxygen the body has actually consumed. That gap became one of the classic ways to estimate the anaerobic contribution to energy production. In 1965, researchers introduced a concept known as critical power, which gave physiologists another way to think about the relationship between sustainable aerobic power and a finite amount of work that can be done above that threshold. This new review is really an updated attempt to synthesize decades of work built on those ideas.

The authors systematically searched seven databases and ended up including 102 studies, which together provided 311 data points from maximal exercise trials. Most of the data came from cycling and running, though the review also included swimming, kayaking, skiing, rowing, climbing, and arm-crank exercise. The participants were mostly trained adults, mostly male (unfortunately common in exercise physiology research), with an average age of about 24 years. The exercise bouts ranged from 6 seconds to more than 28 minutes. Then the authors modeled how the relative aerobic and anaerobic contributions changed across exercise duration.

One thing I appreciated is that the paper doesn’t pretend this is an easy measurement problem. A quick explanation…

Aerobic contribution is relatively straightforward to estimate because oxygen uptake during exercise can be measured directly (hence why you can get your maximal aerobic capacity measured in a lab). Anaerobic contribution is where things get messy. The review grouped studies into three main categories of how they measured it: oxygen deficit methods, mixed methods, and theoretical models.

All of these are useful, but none of them is perfect. In fact, the authors are very clear that there is still no universally accepted gold standard for quantifying anaerobic energy release during whole-body maximal exercise. Despite all of that methodological messiness, the overall pattern that emerged was pretty consistent. The model estimated that:

A 10-second maximal effort is about 91% anaerobic and 9% aerobic.

At 20 seconds, it’s 82% anaerobic and 18% aerobic.

At 30 seconds, it’s 75% anaerobic and 25% aerobic.

By 60 seconds, the effort is already estimated at 58% anaerobic and 42% aerobic.

Then comes the number that’s the cornerstone of the review. Equal aerobic and anaerobic contribution occurs at about 78.6 seconds. By 90 seconds, the aerobic contribution has already moved ahead, at roughly 54% aerobic and 46% anaerobic. At 2 minutes, the review estimates about 62% aerobic. At 3 minutes, about 72% aerobic. At 5 minutes, about 81% aerobic.

For me, the headline of the paper is that the aerobic system becomes important much earlier than most people assume. And it challenges the lazy version of the aerobic vs. anaerobic story.

We often talk as if short, hard exercise is basically anaerobic until some later point when the aerobic system finally joins the party. But the review makes clear that oxidative metabolism ramps up quickly during intense exercise.

The aerobic system is not a backup generator that kicks in late. It is contributing meaningfully even in very short maximal efforts, and its contribution rises fast. The authors also point out that oxygen uptake kinetics are faster during high-intensity exercise than traditionally appreciated, which helps explain why aerobic contribution is already substantial within the first minute or so.

The paper is careful not to oversell precision, which I think is important. These percentages are modeled estimates built from several different studies with varying exercise modes, protocols, and pacing strategies. So I wouldn’t treat 78.6 seconds as some magical physiological law written in stone. But the general conclusion is pretty persuasive—anaerobic energy supply is finite, aerobic energy supply is ongoing, and the balance between the two shifts earlier and more rapidly than a lot of our conventional thinking suggests.

In short, our old binary framing is too rigid.

The phosphagen, glycolytic, and oxidative energy systems are all involved from the outset of exercise. So the real question is not whether an effort is aerobic or anaerobic. The better question is how much each system is contributing, how quickly that balance changes, and what that means for performance and fatigue. That’s a much better (and much more interesting) way to think about exercise physiology, in my opinion.

Thanks for reading. See you next Friday.

~Brady~

Ketone-IQ is high-performance energy in a bottle. I use it for post-exercise recovery along with enhancing focus, mood, and cognition. Take 30% off your order.

Create is the first “modern creatine” brand. They sell a wide range of creatine monohydrate gummies, and just launched a new creatine + electrolyte mix product plus travel-size packets of creatine gummies. They’re giving my audience 20% off their order.

ProBio Nutrition—the all-in-one supplement that I use every single day—is offering 20% off. My preference is the tangy orange flavor, but they also sell an unflavored “smoothie booster” that’s great in a shake, smoothie, or juice.

• Equip Foods makes some of the cleanest, best-tasting protein products around. I am absolutely obsessed with their Prime grass-fed protein bars (the peanut butter ones are to die for, but they also just released a new chocolate peanut butter flavor).

Greetings!

Welcome to the Physiology Friday newsletter.

Details about the sponsors of this newsletter and deals on products I love, including Ketone-IQ, Create creatine, Equip Foods, and ProBio Nutrition can be found at the end of the post.

One of the oldest questions in exercise science is why some people seem to improve dramatically with training while others improve only modestly or, at least on paper, not at all.

That debate sits at the center of the long-running idea of “non-responders” to exercise. It’s compelling, but also contested.

This concept comes from studies showing that when a group of people completes the same training program, some increase their cardiorespiratory fitness (their VO₂ max improves) while others show little change. In some cases, a few participants even appear to get worse… but that apparent decline is often more likely to reflect measurement error, day-to-day biological variability, or statistical noise than a true drop in fitness.

That’s one reason the whole idea of a true “non-responder” remains hotly debated. And I’m skeptical of it myself.

A lot of research shows that when so-called non-responders are given a larger dose of exercise (more weekly volume, a higher frequency of sessions, or more intensity), they often do respond. Their cardiorespiratory fitness improves. In other words, they may not be biologically incapable of adapting; they may simply need a different stimulus.

Probably one of my favorite exercise science studies ever was one that challenged the whole idea of “non-responders” to training. In the study, researchers took 78 healthy adults and had them complete the same endurance training program, but with different weekly doses ranging from 60 to 300 minutes per week. The more people exercised, the fewer of them were classified as non-responders.

The people initially labeled as non-responders then went on to do another 6 weeks of training, this time with an extra 120 minutes of exercise per week. Once the dose increased, they responded… non-response essentially disappeared. To me, that is such an important finding because it suggests that many so-called non-responders are probably not true non-responders at all. They may just be people who need a larger training stimulus to cross whatever threshold is required for adaptation. That idea makes a lot more physiological sense to me than the idea that exercise just somehow doesn’t “work” for a meaningful subset of people.

Putting this study aside, the idea that we all respond differently to the same training stimulus makes sense. Human physiology is variable. Biology is variable. It would be surprising if every person responded the same way to the exact same training plan. So the real question is not whether responses differ—they clearly do—but why.

What explains why one person responds robustly while another barely budges? Are there underlying biological differences driving that variability? Could genetics play a role? And are there measurable molecular or physiological markers that could help predict who is likely to respond to a given exercise program?

That is the premise of a new study pointing to a potential biomarker in the blood: a protein called insulin-like growth factor binding protein 7, or IGFBP7. In humans, higher levels predicted a worse response to training and were even associated with a range of cardiometabolic diseases. And in mice, deleting this “molecular brake” produced an enhanced training response.1

Let’s get into it.

The study comes from a multi-institutional group that included researchers from Massachusetts General Hospital, Harvard, the University of Michigan, and the Norwegian University of Science and Technology. Their work builds on the Generation 100 trial in Norway, which was a randomized clinical trial involving 1,567 older adults with an average age of 73 years.

The researchers focused on the trial’s high-intensity interval training (HIIT) arm. Participants performed two supervised HIIT sessions per week for a full year, with each session consisting of four 4-minute intervals at 90% of peak heart rate, separated by 3-minute active recovery periods (it’s the classic Norwegian 4x4 interval workout).

The variation in the responses to HIIT was enormous. After a year of identical training, changes in peak VO₂ ranged from a 56% increase to a 28% decline.

That naturally raises a deeper question and goes back to the idea of exercise non-responders we talked about earlier. What is going on biologically in the people who fail to adapt?

IGFBP1 is a “molecular brake” on fitness

To figure out what might be blocking adaptation, the researchers moved from whole-body performance to the blood. They selected the extremes—10 high responders, who improved peak VO₂ by an average of 22%, and 10 low responders, who showed no gain or a decline. Crucially, they matched them carefully before training. Same age. Same sex. Same baseline fitness. Same lean mass. This ensured that the difference between these people was not apparent in their outward phenotype.

The team measured 305 proteins under four conditions:

• At rest before training,

• Immediately after acute maximal exercise before training,

• At rest after the year-long training intervention, and

• Immediately after acute maximal exercise after training.

Now we can understand how the proteome responded to acute stress and how it changed over time after long-term training.

One of the first really important findings was that low responders were not failing to register the workout. Acute exercise produced major proteomic shifts in both groups. In fact, 659 proteins changed in high responders, while 783 proteins changed in low responders. So the people who ultimately failed to improve were not “responding less” to exercise. Their bodies absolutely detected the stress, and if anything, the acute perturbation was even larger.

After 12 months of HIIT, the high responders showed 394 significant changes in their resting proteome. Their biological baseline had changed. The low responders, by contrast, showed zero significant changes in their resting proteome. Zero. I think that is one of the most striking findings in the paper. Both groups repeatedly experienced acute stress and mounted an immediate response to exercise. But only one group appeared to ingrain this into their physiology and actually “remodel” their resting proteome.

The next step was to ask whether future responsiveness could be predicted before training even started. Using the baseline blood samples alone, the researchers identified 228 proteins that were associated with later fitness gains. And among all of these candidate proteins, one stood out most clearly: IGFBP7, or insulin-like growth factor binding protein 7.

IGFBP7 showed a striking inverse relationship with training response. Higher baseline levels predicted worse gains in fitness, and lower baseline levels predicted better gains. Baseline IGFBP7 accounted for about 80% of the variance in trainability among the participants. That is an enormous signal.

This gets even more interesting (okay, at least to me) when we consider what IGFBP7 actually is. The protein is classified as a senescence-associated secreted protein, or SASP factor, which means it is part of the inflammatory and suppressive cocktail released by senescent cells—the so-called “zombie cells” that accumulate with age. That provides a direct biological bridge between aging and blunted adaptation to exercise.

Of course, a strong correlation does not prove causation. But the problem is, without genetic engineering (which presents its own ethical problems and isn’t yet acceptable for most human research), we can’t really establish causation in humans.

So the authors turned to mouse models and started manipulating the system directly. They used IGFBP7 knockout mice, animals genetically engineered so they could not produce the protein at all. Interestingly, these mice had 14–15% less muscle mass in the heart and leg muscles, and the female knockout mice had 61% more fat mass than controls (who were able to produce IGFBP7). Yet despite these differences in body composition, the knockout mice had similar baseline exercise capacity to normal mice (just like the human study).

Once the mice started training, the difference became obvious. In a treadmill training protocol, the knockout mice improved dramatically more than wild-type controls. After four weeks, they gained 3.3 times more fitness overall. Male knockouts gained 5.7 times more.

The researchers then tested whether this advantage would hold up in a different kind of exercise setting. Instead of forced treadmill work, they gave the mice voluntary running wheels. Over four weeks, the animals ran 5–7 kilometers per day, which for a mouse is a huge training volume. Both groups improved more than they did on the treadmill, but the knockout mice still outperformed the wild-types by 54%. By week two, the knockouts had already pulled away. So the absence of IGFBP7 raised both the ceiling of adaptation and accelerated the kinetics, with mice adapting faster and to a greater extent when they lacked IGFBP7.

But there is still an obvious caveat with knockout models: if you remove a gene starting from conception, maybe the animal develops differently in all kinds of ways unrelated to the protein’s immediate role in adulthood. The researchers addressed that too. They increased circulating IGFBP7 in adult mice by about fourfold. Even though the manipulated mice still ran the same distances on the wheel (exercise behavior didn’t change), the enhanced trainability disappeared. This suggests IGFBP7 is not merely a biomarker of poor adaptation. It appears to actively constrain it.

So what is this protein actually doing?

IGFBP7 seems to act like a systems-level brake on several core components of exercise adaptation. One major axis is muscle growth and repair.

• The paper points to evidence that IGFBP7 interferes with insulin-like growth factor-1 (IGF-1) signaling, essentially blocking one of the body’s main anabolic pathways. In the knockout mice, circulating IGF-1 was 40% higher, and the animals gained 9% more muscle mass across multiple muscles during training.

• Another major axis is blood vessel growth. Trained knockout mice had about double the increase in capillary density compared with controls.

• The study also found broad metabolic adaptations across the muscle, heart, and liver. With training, the knockout mice appeared to shift into a more advanced fuel-handling strategy, exhibiting greater fatty acid metabolism in skeletal muscle, more Krebs cycle and amino acid breakdown in the heart, and enhanced aerobic and fat metabolism in the liver.

The final layer of the paper zooms out from exercise performance to long-term health (something we are all interested in) using UK Biobank data from 52,033 participants followed for about 16 years. After adjusting for age and sex, lower IGFBP7 levels were associated with a lower risk of all-cause mortality, heart failure, COPD, cirrhosis, chronic kidney disease, and type 2 diabetes. When participants were grouped based on their baseline levels of IGFBP7, those with the highest levels had a 2.9- to 11-fold greater disease rate than those with the lowest levels across these outcomes.

There’s a bit of a philosophical conclusion to this paper that I really dig.

The authors propose that trainability may reflect something deeper than exercise response per se. It may be a visible readout of cellular resilience. A hard workout is, at the cellular level, a controlled crisis. Body temperature rises, blood acidity shifts, energy demand spikes, and blood flow stress changes rapidly. Those same kinds of disturbances also show up in disease states.

The same molecular systems that determine whether your body responds to a training block may also help determine how well it resists heart failure, kidney disease, diabetes, or the slow erosion of function that comes with age. And IGFBP7 may be one of the clearest molecular signals yet identified that helps explain why. In fact, this could be one potential explanation for the broader link between VO₂ max and longevity that I’ve covered so extensively. While I do think that aerobic fitness is protective in the sense that it reflects the body’s overall capacity to coordinate oxygen delivery and energy production, this paper would suggest it may also reflect physiological plasticity—the processes that allow someone to obtain a high level of fitness in the first place are also important for guarding against death and disease.

This does not mean the obvious next step is to block IGFBP7 and call it a day. The therapeutic angle is much trickier than that. Because IGFBP7 is tied to senescence and growth control, lowering it could come with serious tradeoffs (prior work has shown that knockout mice can develop certain types of cancer). So the same brake that may limit adaptation could also exist for a reason (biology isn’t so dumb after all). It may prevent a system from becoming too permissive. The paper raises the possibility that transient or context-specific inhibition could one day help older or frail individuals get more biological return from lower-intensity activity, but that is still a speculative therapeutic vision.

Still, even setting aside the pharmaceutical angle, this is a fascinating piece of science. It gives us a candidate molecular explanation for one of the most persistent mysteries in exercise physiology. It links age-related senescence biology to adaptation and shows that the body can fully register the stress of exercise and yet still fail to remodel. And it suggests that trainability may be less about motivation or willpower than about whether someone (or some mouse) is biochemically capable of upgrading itself in response to stress.

More than anything, this paper makes me think about aging a little differently.

We often think of aging as the slow accumulation of damage. But studies like this hint that maybe aging is also a progressive loss of adaptive capacity. If that is true, then the future of healthy aging may depend on preserving the body’s ability to adapt in the first place. And this study suggests IGFBP7 might be one of the proteins sitting directly in the middle of that story.

Thanks for reading. See you next Friday.

~Brady~

Ketone-IQ is high-performance energy in a bottle. I use it for post-exercise recovery along with enhancing focus, mood, and cognition. Take 30% off your order.

Create is the first “modern creatine” brand. They sell a wide range of creatine monohydrate gummies—and yeah, their gummies actually contain creatine, unlike some other brands. They’re giving my audience 20% off their order.

ProBio Nutrition—the all-in-one supplement that I use every single day—is offering 20% off. My preference is the tangy orange flavor, but they also sell an unflavored “smoothie booster” that’s great in a shake, smoothie, or juice.

• Equip Foods makes some of the cleanest, best-tasting protein products around. I am absolutely obsessed with their Prime grass-fed protein bars (the peanut butter ones are to die for). Take 35% off a subscription.

Greetings!

Welcome to the Physiology Friday newsletter.

Details about the sponsors of this newsletter and deals on products I love, including Ketone-IQ, Create creatine, Equip Foods, and ProBio Nutrition can be found at the end of the post.

Today, I’m discussing what I think is probably one of the most important studies on sleep in the last decade.

It suggests that the timing and consistency of your sleep (especially when you go to bed) may be just as important as how long you sleep.

Sleep health research has been handicapped because we’ve been looking at a question that’s much too simple: how much sleep do people get?

That question has produced an enormous literature linking both short (and long) sleep duration with cardiometabolic disease, mortality, impaired glucose regulation, hypertension, and obesity… among other maladies. “The shorter your sleep, the shorter your life.”

But duration increasingly looks like only one dimension of sleep health. A person can sleep for an apparently adequate number of hours while still living in a state of circadian misalignment—going to bed at widely varying times, waking at inconsistent hours, and forcing their physiology to adapt to shifting time schedules.

The emerging idea is that sleep may not merely be a matter of quantity or even quality, but also of consistency and regularity… whether the timing of sleep is stably embedded within your body’s natural 24-hour cycle.

Cardiovascular physiology is profoundly circadian. Blood pressure, endothelial function, hormone secretion, glucose handling, and even our susceptibility to heart attacks (they’re more common in the morning) all fluctuate across the day-night cycle. When behavior repeatedly misaligns with those oscillations—as in shift work, late chronotypes (“night owls”), or erratic sleep timing—the result is far worse than just poor sleep. For systems like the cardiovascular system that depend on some level of “biological predictability,” this may spell disaster for long-term health.

In fact, the American Heart Association has recently updated its stance on sleep to reflect this emerging data.1 Their recent scientific statement marks a clear shift from treating sleep mainly as a duration issue to treating it as a multidimensional health behavior, with sleep regularity elevated to a core component of cardiometabolic health. The AHA explicitly frames healthy sleep as including not just duration, but also continuity, timing, regularity, daytime functioning, sleep architecture, and the absence of sleep disorders. Regularity is now being emphasized alongside quantity and quality as something that may shape blood pressure, glucose regulation, adiposity, and other cardiometabolic pathways.

The authors of a new study position sleep timing regularity within exactly this framework… as a measurable expression of circadian stability. And they find that regularity matters, a LOT. Especially if you’re not sleeping enough.2

The study, from the Northern Finland Birth Cohort 1966, asks a more specific question than much of the existing sleep-regularity literature. Rather than relying only on composite (and frankly difficult to understand) metrics such as the Sleep Regularity Index or interdaily stability (which are inherently hard to calculate and even harder to implement practically), it isolates three highly interpretable timing variables:

• Bedtime

• Wake-up time

• The midpoint of the sleep period

A standard deviation in bedtime or wake-up time is immediately legible as a behavioral pattern. If risk concentrates there, the biology becomes easier to interpret, and the intervention target becomes more concrete. And that’s one of the reasons this study is so knowledgeable and practical.

The cohort included 3,231 adults, all around midlife, drawn from participants in the 46-year follow-up of the study conducted in 2012–2014. These individuals wore a wrist-based activity monitor continuously for up to two weeks, and sleep timing variables were derived using a sleep-detection algorithm that’s been validated against both self-reported sleep diaries and smart-ring-derived sleep periods. In other words, it’s pretty accurate at assessing sleep.

The authors used the first seven consecutive days of valid monitoring, reasoning that a seven-day window captures both weekday and weekend structure and is sufficient to estimate someone’s habitual timing variability.

Bedtime, wake-up time, and sleep midpoint were each quantified as the standard deviation across those seven days—the main outcome being sleep timing regularity. Participants were categorized into one of three sleep timing regularity categories:

• Regular

• Fairly regular

• Irregular

To get a better idea of how “regular” or “irregular” these people’s sleep patterns were, let’s look at the median values for sleep timing across each of these groups. In other words, how much did bedtime, wake-up time, and the midpoint of sleep vary from night to night?

• For regular sleepers, bedtime had a variability of 33 minutes, wake-up time had a variability of 41 minutes, and the midpoint of sleep had a variability of 33 minutes.

• For fairly regular sleepers, variability for bedtime, wake-up time, and sleep midpoint was 59 minutes, 73 minutes, and 55 minutes, respectively.

• For the irregular sleepers, variability was 108 minutes, 114 minutes, and 93 minutes for bedtime, wake-up time, and sleep midpoint, respectively.

Let’s put this in perspective. This means that someone with an irregular sleep schedule might have a bedtime or wake-up time that changes by 1.5 to 2 hours from night to night. They might usually go to bed at 11 pm, but might also go to bed at 9 pm on some nights and as late as 1 am on others. But someone with a regular schedule and an average bedtime of 11 pm will be in bed between 10:30 and 11:30 every single night.

The primary endpoint of this study was major adverse cardiac events (obtained from a national health registry), defined as the first time someone experienced acute myocardial infarction, unstable angina, stroke, hospitalization for heart failure, or cardiovascular death.

The authors also divided participants based on the median sleep duration of the group—7 hours 56 minutes—creating below-median and above-median sleeper groups (let’s just refer to them as “short sleepers” and “normal sleepers.”

During a follow-up period of about 130 months, 128 participants—4.0% of the sample—experienced a major cardiovascular event. But surprisingly, those who had an event did not differ significantly from those who didn’t in average bedtime, average wake-up time, sleep midpoint, or how many hours they slept each night. On the surface, then, there was no simple signal that people who later developed cardiovascular events were just sleeping later, waking later, or sleeping less overall.

And this is where the paper becomes interesting…

Among participants whose sleep period duration fell at or below the median—7 hours 56 minutes or less—irregular sleep timing predicted a higher cardiovascular risk.

• Compared with those in the most regular bedtime category, participants with irregular bedtimes had twice the risk of experiencing a major cardiovascular event during the follow-up.

• Participants with irregular sleep midpoints also had a 2-fold greater risk of a major cardiovascular event compared to regular sleepers.

• Wake-up time irregularity was not significantly associated with risk.

Importantly, none of the three timing variables predicted cardiovascular events among participants with “normal sleep”—those getting more than 7 hours and 56 minutes of sleep per night!

This is a very particular result, and it somewhat contradicts previous findings that sleep regularity is harmful even among people getting enough sleep. This study wouldn’t support that. It’s only among people sleeping somewhat less than eight hours that the instability of when one goes to bed (and their midpoint of sleep) seems to matter for heart disease risk.

A few more important nuances. Even when the researchers excluded the 12 participants who experienced a major cardiovascular event within the first two years of follow-up, the results did not materially change, which argues against the simplest version of reverse causation (the idea that subclinical cardiovascular disease at baseline was already disrupting sleep timing and driving the association).

Excluding shift workers also did not change the results. In fact, the associations became slightly stronger when shift workers (a group with notoriously altered circadian rhythms) were excluded. After removing shift workers, participants with irregular bedtimes and sleep midpoints had a 2.25–2.3x greater risk of cardiovascular events during the follow-up.

That matters because it suggests irregular sleep timing in ordinary life—outside classic occupational circadian disruption—may itself mark a meaningful health hazard. It wasn’t the shift workers in the study “artificially” driving the association.

One of the more intriguing (and to me, surprising) features of the study is the interaction, or at least the modification, of sleep duration. The association appears only in those with “short sleep.” This can be read in two ways.

One interpretation is that irregular timing becomes especially harmful when it is layered onto relatively shorter sleep, because the combination compounds insufficient recovery and circadian disruption.

Another is that longer sleep may buffer some of the physiological consequences of timing irregularity. The authors lean toward the latter possibility in their own conclusion, suggesting that sufficient sleep may offer partial protection against an irregular bedtime.

And I think this matters for how you should think about your own sleep. If you aren’t getting enough sleep each night, then your strategy needs to be to anchor when you go to bed as close to the same time as possible every night. But if you’re getting enough sleep, you have more “wiggle room.”

The study suggests that not all components of sleep regularity are equivalent. It may be the consistency of initiating sleep—or the consistency of the overall phase of the sleep episode—rather than the consistency of waking per se, that carries more relevance to cardiovascular health. Why might that be?

One plausible explanation is that bedtime is the “entry point” into the nighttime restorative window, whereas wake-up time is often a more downstream, socially constrained event. Sleep onset is tightly coupled to circadian phase. People typically fall asleep as melatonin is rising, core body temperature is falling, and the circadian system is shifting toward the nighttime state.

If bedtime is pushed around from night to night, that phase relationship becomes unstable, so the person is not just sleeping at different clock times but repeatedly asking the cardiovascular, endocrine, and autonomic systems to enter sleep from different circadian states (different “body times”).

A second reason bedtime may matter more is that it likely has a greater influence over sleep architecture and early-night recovery biology. Slow-wave sleep and slow-wave activity are concentrated in the early part of the night, whereas REM sleep predominates later in the night. So when bedtime shifts later or becomes erratic, the body may repeatedly alter the alignment between the first part of sleep and the conditions that normally favor restorative processes. Wake-up time variability, in contrast, may mostly affect the tail end of sleep and may be less “biologically informative” if many people still wake at roughly similar times because of work or family obligations. In that sense, bedtime irregularity may be a better marker of chronic circadian misalignment and of disrupted access to the most important portion of sleep.

We have to note one of the main limitations here. Sleep timing regularity was measured over only seven days at baseline, which may or may not capture an individual’s longer-term habitual behavior across seasons, stressors, or life transitions. How someone slept during one particular week might not reflect how they slept during the nearly 11-year follow-up period.

What this paper ultimately does is suggest that cardiovascular sleep epidemiology (and our own lifestyle practices) may need to move beyond how long people sleep and even beyond when they usually sleep, toward how stably sleep is placed from one day to the next. Regularity and consistency should become a focus of sleep health equal to that of sleep duration.

For now, this study does not argue that sleep regularity matters more than every other cardiovascular risk factor, but it does show that the stability of sleep—specifically bedtime and sleep midpoint—predicts future major cardiovascular events in a way that average sleep timing does not.

I think this may prove to be one of the most consequential conceptual shifts in modern sleep health—from thinking of sleep as a quantity, to understanding it as a rhythm.

Thanks for reading. See you next Friday.

~Brady~

Ketone-IQ is high-performance energy in a bottle. I use it for post-exercise recovery along with enhancing focus, mood, and cognition. Take 30% off your order.

Create is the first “modern creatine” brand. They sell a wide range of creatine monohydrate gummies—and yeah, their gummies actually contain creatine, unlike some other brands. They’re giving my audience 20% off their order.

ProBio Nutrition—the all-in-one supplement that I use every single day—is offering 20% off. My preference is the tangy orange flavor, but they also sell an unflavored “smoothie booster” that’s great in a shake, smoothie, or juice.

• Equip Foods makes some of the cleanest, best-tasting protein products around. I am absolutely obsessed with their Prime grass-fed protein bars (the peanut butter ones are to die for). Take 35% off a subscription.

VO₂ max is probably one of the topics I’ve written about most. And hopefully, I’ve not beaten a dead horse here. I just think there’s endless interest in it, and I don’t think it’s the “fad” people make it out to be.

Even though the importance of VO₂ max has been underscored by myself and others on numerous occasions, VO₂ max has made its way back into the mainstream conversation for a different reason—people are questioning its validity as a true “longevity biomarker.” Because of that, I felt like it would be nice to provide a “state-of-the-science” update.

I’ve been doing the podcast rounds lately, and it has allowed me to brush up on the research regarding VO₂ max as a longevity biomarker. That’s what this post is about, plus the most up-to-date research on how to improve it with training and other tactics. If you want a somewhat deeper dive into the science, you can pick up my book “VO₂ Max Essentials” on Amazon.

Why VO₂ max still matters

VO₂ max remains one of the most discussed metrics in exercise physiology, and for good reason. It is the maximal rate at which the body can take up, transport, and utilize oxygen during intense exercise, and it is widely regarded as the gold-standard laboratory measure of cardiorespiratory fitness (CRF). The American Heart Association reinforced the clinical importance of CRF in their latest update, arguing that it should be routinely assessed in healthcare and even framing it as a clinical vital sign.1

That said, VO₂ max is often discussed too loosely. It is not a standalone “longevity score,” and it is not interchangeable with every estimate of aerobic fitness produced by a treadmill test, population equation, or (especially) wearable. It is a specific physiological construct that we should interpret with nuance.

What VO₂ max actually reflects

Physiologically, VO₂ max is best understood as an integrative property of the oxygen transport system. It depends on pulmonary gas exchange, cardiac output, blood oxygen carrying capacity, distribution of blood flow, and the ability of working muscles to extract and use oxygen. It reflects how well the body can deliver and utilize oxygen when the demand for aerobic energy production is at its highest (e.g., during maximal exercise).

That systems-level nature is what, in my opinion, makes VO₂ max so compelling.

It is not simply a marker of “cardio” in the casual sense. It is a summary readout of how multiple organ systems perform together under stress. At the same time, it does not fully determine endurance performance on its own. Endurance outcomes also depend heavily on economy, durability, and the fraction of VO₂ max that can be sustained near threshold, hence why the guy or girl with the highest VO₂ max doesn’t always win the race.

Where the longevity evidence is strongest

There’s no question that a higher level of fitness is associated with a lower risk of dying.

A 2024 overview of meta-analyses2 synthesized 26 systematic reviews representing 199 unique cohort studies and more than 20.9 million observations.

• Across those data, higher CRF was strongly and consistently associated with lower all-cause mortality, lower cardiovascular mortality, and lower risk of several chronic disease outcomes.

• High versus low CRF was associated with a 53% reduction in all-cause mortality risk, and each 1 metabolic equivalent (1-MET, equal to 3.5 ml/kg/min of oxygen uptake) was associated with an 11% to 17% lower risk of all-cause mortality depending on the meta-analysis.

Those are impressive associations, but the certainty should not be overstated. The same overview rated the certainty of evidence across outcomes from very low to moderate. That does not weaken the overall signal, which is clearly strong and consistent, but it does argue for some caution when translating these epidemiologic associations into sweeping mechanistic claims. And I should note that many studies here were not actually about directly measured VO₂ max. They are about CRF more broadly. So let’s discuss that…

VO₂ max and CRF are related, but they are not interchangeable

A recent source of controversy is not whether aerobic fitness is important, but whether VO₂ max is a useful, meaningful marker worth measuring (separately from CRF, that is).

This arose because many of the most cited mortality studies do not rely on direct breath-by-breath laboratory measurement of VO₂ max. Instead, they often use objectively measured exercise capacity expressed in METs, treadmill-estimated CRF, or even non-exercise CRF estimation models.

How much does that matter? Maybe not a lot if we’re talking strength of associations.

A 2025 systematic review and meta-analysis3 of 42 studies representing 35 cohorts and 3.8 million observations found:

• Objectively measured and estimated CRF showed broadly similar dose-response associations with all-cause and cardiovascular mortality.

• Per 1-MET higher CRF, all-cause and cardiovascular mortality dropped by 14% and 16%, respectively, without statistically significant differences between objective and estimated approaches.

That is important because it supports the broader conclusion that fitness is a powerful risk marker, regardless of how it’s measured. Nevertheless, the epidemiology is strongest for CRF as a construct. VO₂ max is the gold-standard laboratory expression of that construct, but the two are not perfectly synonymous in the literature. I’m willing to accept that, but I’m not quite convinced the semantics matter if we’re talking about longevity in the general sense.

Is VO₂ max a biomarker?

Whether VO₂ max should be called a biomarker depends largely on how the term is being used. If the intended meaning is an objective, reproducible measure associated with future health outcomes, then VO₂ max or CRF clearly behaves like one.

If the intended meaning is a singular mechanistic readout of biological aging, the label becomes much harder to defend. The American Heart Association’s framing is more conservative and more useful: CRF is a vital sign because it provides meaningful prognostic information that improves risk assessment and clinical management. That is, it tells you more about your health when paired with common cardiovascular disease biomarkers (and probably more on its own than any single risk factor like cholesterol, blood glucose, or whether or not you smoke).

I think the most defensible description is that VO₂ max is an integrative functional marker. It compresses a great deal of physiology into one number, but it does not function like a magic molecular biomarker that fully explains why one person lives longer or ages better than another.

How to reliably improve VO₂ max

Read more

Greetings!

Welcome to the Physiology Friday newsletter.

Details about the sponsors of this newsletter and deals on products I love, including Ketone-IQ, Create creatine, Equip Foods, and ProBio Nutrition can be found at the end of the post.

The fitter you are, the less likely you are to die of nearly anything.

That’s not a controversial statement at this point.

The associations between cardiorespiratory fitness and a lower risk of cardiovascular disease, metabolic disease, cancer, and all-cause mortality are well established. I’ve written about that plenty before (you can find the latest example here).

But while the body-side benefits of fitness are widely appreciated, the brain side is still catching up in the broader conversation.

We already know that what is good for the heart is often good for the brain. Better vascular health, better metabolic health, better mitochondrial function, better inflammatory control… these are not body-only phenomena. The brain is downstream (technically upstream, directionally speaking) of all of them. So it’s not a stretch to think that better cardiorespiratory fitness may also confer some protection against mental disorders, neurodegeneration, or age-related cognitive decline.

That idea gets even more interesting when you make the important distinction that fitness is not the same thing as exercise. I say it’s interesting because there’s overwhelming evidence that participating in exercise and physical activity directly confers benefits to mental and cognitive health.

But exercise is the behavior. Fitness is, in part, the physiological result.

If higher cardiorespiratory fitness itself is associated with a lower risk of depression, dementia, or other neuropsychiatric conditions, that suggests something more than “people who exercise are healthier.” It suggests that the physiological machinery that allows someone to achieve and maintain better aerobic fitness (the integrated function of the heart, lungs, vasculature, skeletal muscle, and cellular energetics) may also track with better long-term brain and mental health.

The idea is compelling and logically sound, but as always, better to have data than assumptions.

A new systematic review and meta-analysis provides the most up-to-date evidence on the link between aerobic fitness and long-term brain health, asking whether people with higher cardiorespiratory fitness are less likely to go on to develop mental disorders or dementia later in life.1

This was a meta-analysis of 22 cohort studies involving more than 4 million participants, generally between 18 and 64 years old, who had fitness assessed at baseline and were then followed for anywhere from 4 to 29 years. Fitness was measured in several different ways across studies. Some cohorts used direct VO₂ max testing, others used indirect maximal or submaximal exercise tests, and others used estimated or field-based metrics like METs, peak workload, exercise duration, or distance covered. So while the paper is about cardiorespiratory fitness, it is not exclusively about directly measured VO₂ max (this has become an especially important nuance to point out, since some folks on social media get angry if one doesn’t specify).

Across studies, higher cardiorespiratory fitness was associated with a substantially lower future risk of depression, all-cause dementia, and psychotic disorders, but not clearly with anxiety.

Compared to people with low fitness, those with high fitness had:

• a 36% lower risk of depression.

• a 39% lower risk of all-cause dementia.

• a 29% lower risk of psychotic disorders.

And when fitness was analyzed continuously rather than categorically, each 1-MET (3.5 ml/kg/min) increase in cardiorespiratory fitness was associated with a 5% lower risk of depression and a 19% lower risk of dementia. This is perhaps the more important stat in my opinion, because it shows how a small but meaningful fitness bump affects long-term disease risk (we don’t have hard numbers for the low- versus high-fitness categories, unfortunately).

The results were also broadly similar across several subgroup analyses, including people above and below age 50, men and women, and studies using indirect maximal or submaximal testing rather than direct lab-based VO₂ max measures.

The authors also discussed several outcomes that were not meta-analyzed because only one or two studies were available. Even so, those individual studies suggested that higher fitness may also be associated with lower risk of bipolar-related disorders, dissociative disorders, obsessive-compulsive and stressor-related disorders, somatic symptom disorders, sleep apnea, as well as anxiety and ADHD in children. Depression in girls also appeared lower with higher fitness.

So taken at face value, the signal is broad: higher cardiorespiratory fitness seems to track with lower risk across a wide range of psychiatric and neurocognitive outcomes.

That said, this is still observational evidence, and observational evidence always comes with some limitations.

The first is what’s known as residual confounding. Even in well-adjusted cohort studies, there are always variables that are incompletely captured or not captured at all.

Factors like genetic predisposition, chronic pain, other comorbidities, social support, smoking, diet, and general physical activity habits are potential confounders. It is entirely possible that higher-fitness individuals differ from lower-fitness individuals in multiple ways beyond fitness itself, and some of those differences could independently influence mental health or dementia risk.

The second is reverse causation, which is especially important here.

Early or subclinical symptoms of depression, neurodegeneration, or other psychiatric disease may reduce motivation, physical activity, energy levels, or exercise capacity before a formal diagnosis is ever made. In that scenario, lower fitness would not necessarily cause later disease. It might instead be an early marker of disease already beginning to develop.

The included studies tried to address this by excluding people with diagnosed conditions at baseline, but that only goes so far. Disease can exist for years before diagnosis, especially in dementia.

So while the associations are compelling, they are not proof that improving fitness will directly prevent depression, dementia, or psychotic disorders. To know that, we’d need study designs that measure aerobic fitness at several time points over years to decades to see whether increasing or decreasing fitness is associated with a lower or higher disease prevalence. Ideally, we’d want a randomized controlled trial lasting two decades or more… a guy can dream.

And to the authors’ credit, they do not overclaim. They conclude that higher cardiorespiratory fitness appears to be associated with a lower risk of several mental and neurocognitive disorders, and cardiorespiratory fitness may be a useful candidate marker for risk stratification.

The biological plausibility is real

Even though causality is not established in an observational study like this, the biology here is not hand-wavy. There are several plausible mechanisms linking higher cardiorespiratory fitness to better mental and neurocognitive outcomes, including greater neuroplasticity (the brain’s ability to “rewire” itself), improved brain blood flow and vascular function, reduced inflammation and oxidative stress, and a more stable response to stress. There’s also broader exercise-related pathways involving brain-derived neurotrophic factor (BDNF) signaling (Dr. Rhonda Patrick is the go-to source here), angiogenesis, mitochondrial health, immune regulation, and psychosocial factors like self-efficacy and social support. It’s impossible to say “this one mechanism explains the benefit,” as it’s likely a combination of several factors that explain why fitness (and exercise) have tight links to brain health.

To me, one of the most compelling pieces of the story is the structural brain literature.

In an earlier newsletter, I covered a study suggesting that the relationship between aerobic fitness and brain health may be partly structural, not just functional. That study directly measured both VO₂ max and brain myelin content (myelin being the protective and super-important insulation around our neurons) in 125 cognitively normal adults across two aging cohorts. Higher aerobic fitness was associated with greater myelination across much of the brain, with the strongest differences showing up in middle-aged and older adults. Furthermore, fitter people appeared to reach peak myelination later and then lose it more slowly with age. Across the full sample, myelin peaked around age 31, but in the lowest-fitness group, it peaked before age 22, whereas in the highest-fitness group, it didn’t peak until around age 41. Higher cardiorespiratory fitness has also been associated with less grey and white matter deterioration in adults across the lifespan.

Again, this is not proof that raising fitness prevents dementia. But it strengthens the idea that cardiorespiratory fitness may be a meaningful marker of long-term brain-aging risk.

Mental disorders and neurodegeneration are complex, multifactorial, and deeply heterogeneous. So I am not going to be the person who says, “exercise is the answer.”

Maybe it is one answer. And maybe it is an important one. But I am generally skeptical of the overly simplistic line that “exercise is just as good as medication for depression” (or insert your favorite mental health condition). Improving mood, reducing depressive symptoms, and treating major clinical depression are not interchangeable ideas. And while there is good evidence that exercise and physical activity can help with depressive symptoms, I think the health and fitness world sometimes overshoots by making it sound as though all it takes is a little movement. That is too reductive for a condition as complex as depression.

With dementia and Alzheimer’s disease, I am somewhat more bullish on the role of activity and fitness. Not because I think they are magic bullets, but because the pathways involved, including vascular dysfunction, metabolic dysfunction, inflammation, neurodegeneration, and reduced cerebral blood flow, are exactly the kinds of processes that fitness influences. If there is one lifestyle-related trait I would want on my side for healthy brain aging, high cardiorespiratory fitness would be near the top of the list.

And despite VO₂ max and cardiorespiratory fitness getting some pushback lately as “not true longevity markers,” I still do not see the case against them.

If anything, the opposite seems to be happening. The evidence continues to accumulate that cardiorespiratory fitness is associated not only with lower risk of physical disease and premature death, but also with better brain health, lower dementia risk, and perhaps lower risk of certain mental disorders as well.

And that matters because healthspan is not only about how well the body can hold up with age, but also about the brain that comes along for the ride (and is really in the driver’s seat).

Thanks for reading. See you next Friday.

~Brady~

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ProBio Nutrition—the all-in-one supplement that I use every single day—is offering 20% off. My preference is the tangy orange flavor, but they also sell an unflavored “smoothie booster” that’s great in a shake, smoothie, or juice.

• Equip Foods makes some of the cleanest, best-tasting protein products around. I am absolutely obsessed with their Prime grass-fed protein bars (the peanut butter ones are to die for). Take 35% off a subscription.

Welcome to the Physiology Friday newsletter.

Details about the sponsors of this newsletter and deals on products I love, including Ketone-IQ, Create creatine, Equip Foods, and ProBio Nutrition can be found at the end of the post.

This week, I’m sharing a video podcast I recorded with my ‘Run Long, Run Healthy’ podcast co-host . We break down a new review on antioxidant supplements for athletes. And while the conversation mainly focuses on runners, I think it applies more broadly!

Here’s a link to a post I wrote on the review for the ‘Run Long, Run Healthy’ newsletter.

Thanks for reading. See you next Friday.

~Brady~

Ketone-IQ is high-performance energy in a bottle. I use it for post-exercise recovery along with enhancing focus, mood, and cognition. Take 30% off your order.

Create is the first “modern creatine” brand. They sell a wide range of creatine monohydrate gummies—and yeah, their gummies actually contain creatine, unlike some other brands. They’re giving my audience 20% off their order.

ProBio Nutrition—the all-in-one supplement that I use every single day—is offering 20% off. My preference is the tangy orange flavor, but they also sell an unflavored “smoothie booster” that’s great in a shake, smoothie, or juice.

• Equip Foods makes some of the cleanest, best-tasting protein products around. I am absolutely obsessed with their Prime grass-fed protein bars (the peanut butter ones are to die for). Take 35% off a subscription.

Greetings!

Welcome to the Physiology Friday newsletter.

Details about the sponsors of this newsletter and deals on products I love, including Ketone-IQ, Create creatine, Equip Foods, and ProBio Nutrition can be found at the end of the post.

I don’t claim to have all the answers when it comes to health and fitness. And I don’t pride myself on being one of those people who tries to “debunk” everything. But when I see some science that I know is clearly being misrepresented, I can’t help but call it out.

That’s what happened this week with a viral post claiming that regular exercise is linked to slower biological aging, but only in people sleeping 7+ hours per night—and that people sleeping under 6 hours who exercise “actually age faster.”

It made for a great (and sadly viral) social-media claim. It’s also not what the study, nor the broader literature, shows.

The post (and paper) in question was a cross-sectional NHANES analysis looking at sleep duration, phenotypic age, and exercise activity in 13,569 U.S. adults. Sleep was self-reported. The paper’s goal was to look at associations between sleep duration and a biomarker-based estimate of biological aging called phenotypic age, and then examine whether exercise participation might modify that relationship. That’s an interesting question. But right away, there are limitations. This was observational, cross-sectional, and based on self-reported sleep. So it cannot tell us that exercise causes faster aging in short sleepers. It cannot even really tell us the direction of the relationship.

And then there’s the bigger problem that the subgroup results that went viral were read backwards.

In the paper’s Figure 4a, the no-exercise group showed positive associations between short sleep, extreme short sleep, and long sleep and phenotypic age (higher phenotypic age is not a good thing in this context). But in the group doing more than 150 minutes per week of exercise activity, short sleep, extreme short sleep, and long sleep actually had a lowerphenotypic age relative to normal sleep in that same activity group!

The authors then say that “the short sleep group with more exercise tended to have a higher phenotypic age.” That’s their interpretation of that figure. But that is very different from saying the study showed that exercising while sleeping under 6 hours “ages you faster.” It didn’t. At minimum, the results are mixed and messy.

Maybe it seems like I’m being nit-picky. But I don’t think posts like this are inconsequential. Especially when they get millions of views. Because it potentially changes health behaviors.

If someone comes away from that viral post thinking, “I’m not sleeping enough, so maybe I shouldn’t exercise either,” that is a genuinely bad takeaway. Sleep matters. A lot. But “sleep matters” is not the same thing as “exercise becomes harmful when sleep isn’t perfect.” Those are very different claims, and the evidence here does not justify the second one. In fact, it suggests exactly the opposite—exercise is protective in the context of insufficient sleep.

One example…

A much better recent study followed 59,078 UK Biobank participants prospectively and used device-measured sleep and physical activity rather than relying only on self-report. It tracked people for a median of 8.1 years and looked at how sleep, moderate-to-vigorous physical activity, and diet interacted to influence lifespan and healthspan. The big picture was not “exercise only helps if sleep is already perfect.” It was that modest concurrent improvements across these behaviors were associated with meaningful gains in lifespan and healthspan. And importantly, the authors noted that MVPA appeared to be the primary contributor to those gains. In fact, several of the lower-sleep, higher-MVPA groups did better on both lifespan and healthspan than groups with more normal sleep but low activity. Sleep still matters, but inactivity is not some neutral fallback position. In a way, you can out-exercise poor sleep, but you can’t outsleep inactivity.

That paper also found that the minimum combined pattern associated with better lifespan and healthspan became apparent once people moved into the moderate MVPA category, regardless of the corresponding sleep and diet patterns. In other words, physical activity was one of the main drivers of benefit.

Of course, the highest gains were seen in people with high MVPA, moderate sleep, and high diet quality, but the broader pattern was clear—activity was protective, not dangerous.

That is why I think the viral interpretation of the first study is technically wrong (at best) and probably harmful (at worst).

A lot of people already have an all-or-nothing mindset with health. If sleep isn’t perfect, they assume the workout doesn’t count. If the diet isn’t perfect, they assume they blew the day. If recovery isn’t perfect, they assume training is pointless. That mindset is already a problem. And a message like “exercise may age you faster if you sleep under 6 hours” is not helping anyone.

The better message is that yes, poor sleep is a problem. Yes, you should work on it. But poor sleep is usually a reason to improve sleep and keep exercising—not a reason to avoid exercise.

To be clear, I’m not saying the first paper is worthless. It adds to the literature showing that abnormal sleep duration is associated with worse health-related outcomes and that the relationship between sleep and biological aging is complicated. It also raises an interesting hypothesis about how sleep duration and activity might interact. But hypothesis-generating is not the same thing as proving a clean public-health message. And it definitely is not the same thing as telling millions of people that exercise might be aging them faster if they didn’t sleep enough the night before.

A paper with self-reported sleep, cross-sectional data, and internally messy subgroup findings became a viral warning against exercise in short sleepers. Meanwhile, the broader literature points in the other direction. Overwhelmingly, physical activity remains one of the strongest protective behaviors we have, including in the context of imperfect sleep.

That’s the real story here.

And unfortunately, the version that spread left people with exactly the wrong idea. And what’s even more frustrating is that dozens of health influencers quoted that same post reinforcing the message, even though it was clear they didn’t even take a glance at the study that was being referenced.

That’s all I have for now. But safe to say this won’t be the last time I make a post like this. Unfortunately.

Thanks for reading. See you next Friday.

~Brady~

Ketone-IQ is high-performance energy in a bottle. I use it for post-exercise recovery along with enhancing focus, mood, and cognition. Take 30% off your order.

Create is the first “modern creatine” brand. They sell a wide range of creatine monohydrate gummies—and yeah, their gummies actually contain creatine, unlike some other brands. They’re giving my audience 20% off their order.

ProBio Nutrition—the all-in-one supplement that I use every single day—is offering 20% off. My preference is the tangy orange flavor, but they also sell an unflavored “smoothie booster” that’s great in a shake, smoothie, or juice.

• Equip Foods makes some of the cleanest, best-tasting protein products around. I am absolutely obsessed with their Prime grass-fed protein bars (the peanut butter ones are to die for). Take 35% off a subscription.

Greetings!

Welcome to the Physiology Friday newsletter.

Details about the sponsors of this newsletter and deals on products I love, including Ketone-IQ, Create creatine, Equip Foods, and ProBio Nutrition can be found at the end of the post.

I became interested in ketones (the exogenous type that you take as a “shot,” rather than the endogenous ones your body produces when you fast or eat a ketogenic diet) several years ago.

They were an up-and-coming supplement in the endurance sports world. Around that time, I also started writing some blog articles for H.V.M.N. (now Ketone IQ, one of the prominent companies in the exogenous ketones space) on the science of ketones, exercise performance, and other topics like fasting and cognition.

(Full disclosure: I sit on the Science Advisory Board of Ketone IQ, because it’s a company I believe in and a group of people I trust and admire.)

Back then, the hype around exogenous ketones centered on their potential as a performance-enhancing supplement, mainly as an alternative energy source (to carbs) during endurance exercise. A few studies at the time showed some modest benefits for time to exhaustion when participants took ketones before or during a prolonged endurance session, and there were even reports that Tour de France cycling teams were ordering ketones by the carton.

The performance fuel side is still one of the major selling points of ketone supplements, and there are definitely some interesting benefits shown in recent studies, like enhanced cognitive performance during ultramarathon running, for example.

But to be honest, I’m less enthused about the ergogenic effects of ketones.

Instead, my enthusiasm lately has been around ketones as a post-exercise recovery tool—a way to help the body adapt better to exercise training over time. The evidence here is incredibly robust (and growing). In fact, I published a review article over 7 years ago, when the first study came out showing that taking ketones during a chronic heavy endurance training block prevented symptoms of overtraining in cyclists.

That single study changed how I thought about (and used) ketones. When taken post-workout or during heavy periods of training, they may act like an “adaptation signal” for the body. Quicker recovery, and potentially fewer symptoms of “I feel like crap.”

But it turns out they may be doing even more than that. According to a recent study, ketones don’t just enhance recovery… they may actually enhance training adaptations, allowing already fit athletes to gain more aerobic fitness, performance, and mitochondrial gains when layered on top of training.1

The research positions post-exercise ketones as “a promising nutritional strategy to amplify mitochondrial remodeling and endurance performance.”

If you want to try ketones for yourself and experience what all the hype is about, I’d encourage you to try out my preferred ketone supplement, Ketone IQ. Visit ketone.com/BRADY for 30% OFF your subscription order plus receive a free gift with your second shipment—fun surprises like a free 6-pack, Ketone-IQ merch, and more (raspberry lemonade, caffeinated and non-caffeinated, is my favorite).

Twenty-eight trained male cyclists (average VO₂peak of ~50–55 ml/kg/min, so pretty fit) completed an 8-week endurance training intervention that comprised two 4-week blocks: 3 weeks of a progressive increase in training load followed by a 1-week “taper” (training load reduced by 40–50%), followed by another 3-week progression and then a final taper week. The participants trained 5 days per week, with 1–2 of those days including 2 training sessions, and they completed a variety of endurance and high-intensity workouts ranging from steady-state (“zone 2”) sessions to all-out 30-second sprints.

Here’s where the groups differed. After every training session and 30 minutes before sleep on training days, one group of participants consumed 25 grams of an exogenous ketone ester (i.e., 50 grams per day on training days). The other group (the control group) consumed a taste-matched placebo.

At baseline (before training), after week 3 of training, after week 7 of training, and at the end of the final taper week (week 8), the participants performed a 30-minute time trial test and a VO₂max test to assess average 30-minute power output, maximal oxygen consumption, and peak power output. Researchers also measured heart function and cardiac output, took muscle biopsies to assess markers of mitochondrial function, and even assessed the athletes’ subjective rest and recovery. It was one of the most comprehensive “performance workups” you could ask for from a study like this.

A few important notes before getting into the main results.

First, training load and diet were the same between the ketone and control groups throughout the study. That’s important because it means we can attribute any differences to the supplement, not to some nuance like the athletes in the ketone group training harder or fueling better (one potential flaw in the “overtraining study” I mentioned earlier was that the ketone group ended up eating more throughout the training intervention). Second, athletes who took the ketone supplement saw a rise in their blood ketone levels after exercise, with blood BHB reaching 2.6 mM. That’s relevant because it gives us a target range when we start to think about the potential applications of this study for our own training and recovery.

As a result of training, both groups improved their average power output during the 30-minute time trial by about 10% after 3 weeks (from 250 watts to 275 watts). But by week 7, the ketone group increased their average power output by 4% compared to the control group (for a final difference of 11 Watts; 302 watts versus 291 watts), a difference that persisted even after the final taper week (week 8 of the study).

Both groups also improved their peak power output by about 5% after 3 weeks of training. But while the control group didn’t further increase peak power output from week 3 to week 7 (they plateaued), the ketone group did, increasing it by an extra 7% from week 3 to week 7. Overall, the ketone group increased their peak power output by 52 watts during the 8-week study, compared to a 25-watt improvement in the control group.

The participants who took ketones also saw a greater improvement in VO₂peak, which increased by 6.4 ml/kg/min from pre- to post-training, compared to a 3.3 ml/kg/min increase in the control group.

So we have greater increases in cycling performance (time-trial power output), peak power output, and peak oxygen consumption in athletes who supplemented with ketones during training—an incredible result in its own right. But that might not even be the most interesting part of this study.

When the researchers looked at markers of muscle mitochondrial content, they observed that the ketone group had more than twice the increase in the activity of citrate synthase, an enzyme considered a marker of mitochondrial content (which rose by 15% in the control group and 32% in the ketone group). They had higher citrate synthase activity overall compared to the control group at the end of the study as well. There was also a 25% increase in the protein content of complex II of the electron transport chain (the energy-producing pathway in mitochondria) in the ketone group, but no change in the control group.

What about the heart? Improvements here didn’t show any between-group differences — athletes in both groups increased peak cardiac output and stroke volume, classic endurance training adaptations. But adding ketones to training didn’t appear to alter structural or functional cardiac adaptations over and above a placebo.

Lastly—and this was something I found surprising—ketones didn’t appear to provide any benefit for subjective stress and recovery scores. All athletes experienced a similar decline in their stress-recovery state throughout the training period (it got worse). I only say this was “surprising” on account of the older overtraining data. I expected the ketone group to at least feel slightly less “stressed” and a bit more recovered.

If you weren’t on the post-exercise ketones bandwagon already, you should be now.

That’s because this wasn’t a study in an untrained group, and it didn’t involve a brutal training intervention designed to drive athletes into the ground.

No—this was a reasonable endurance training intervention conducted in already well-trained cyclists. That’s key for a few reasons, the first of which is that trained athletes have typically squeezed out nearly every last bit of adaptation they can get through training, diet, sleep, or supplementation. Usually, adding a supplement or recovery protocol leads to marginal gains, if any, especially when athletes are already training intensely.

But in this study, adding ketones to an already optimized training plan allowed these athletes to eke out several percentage-point improvements on top of training alone. Usually, those types of gains are reserved for unfit, sedentary people, not fit athletes undergoing routine endurance training.

What’s really exciting about this study is the mechanism: ketones appear to be helping build more mitochondria during training, suggesting they’re driving muscular adaptations rather than central (cardiac) ones. This might seem surprising, but it’s well known that in addition to their role as “energy molecules,” ketones are also signaling molecules that induce a wide array of genetic and epigenetic changes throughout the body with relevance to health, longevity, and now, performance.

So what are the practical applications here? For the recreational amateur athlete like you and me (unless you’re one of the pros who reads my newsletter), I think they’re huge.

While increasing training volume is the clearest and most logical way to gain fitness, not everyone can just “train more.” So if you have a hard cap on your training volume, targeted ketone supplementation around training (post-exercise and pre-sleep) could be the ultimate biological lever—a way to artificially amplify your cellular “return on investment” from the time you spend training.

Ketones appear to be one (but not the only) way to maximize the process of recovery and adaptation to exercise. It’s similar to the benefits I see with post-exercise sauna or hot-water immersion. Not replacements for training, but effective adjuncts, a tool you can leverage alongside training.

That’s what makes them, in my mind, unique and exciting. Some of this research is honestly showing that post-exercise ketones might be the closest thing to a “biohack” we have in the world of endurance sports. Especially when you add this study to the literature that already exists on EPO production and hormonal signaling, which ketones also appear to influence.

Of course, you still have to do the work of sweating through the intervals. But when you’re done, ketones may be there to amplify the effort.

It’s still a growing area of research, and we’ve got a lot to learn and trials to run, including studies in less-fit and more-fit individuals, as well as women.

But I’m bullish on ketones. And if you’re serious about getting more out of your training, they might be something interesting to experiment with.

Thanks for reading. See you next Friday.

~Brady~

Greetings!

Welcome to the Physiology Friday newsletter.

Details about the sponsors of this newsletter and deals on products I love, including Ketone-IQ, Create creatine, Equip Foods, and ProBio Nutrition can be found at the end of the post.

The physical activity guidelines are good. But like most public health recommendations, they’re not perfect.

Part of that imperfection (or at least their real-world ineffectiveness) probably has nothing to do with the guidelines themselves, and everything to do with the fact that most people don’t follow them. Only around 25% of adults in the United States meet the weekly physical activity guidelines for aerobic and strength training.1

So yes, one could argue this reflects a human limitation more than a guideline problem.

But there’s a deeper issue. As I’ve discussed in a prior newsletter—and even talked about with Dr. Rhonda Patrick on her podcast—the guidelines rely on a somewhat anachronistic way of equating exercise intensity. They assume that one minute of vigorous exercise is “worth” two minutes of moderate exercise. That equivalence makes sense for calorie burn. But when it comes to health outcomes and chronic disease risk, vigorous activity’s benefits often look disproportionately (even exponentially) larger than those of light- or moderate-intensity activity.

That brings up another limitation of the guidelines, one that’s especially relevant for disease prevention. They largely assume that everyone, regardless of health status or disease risk, will benefit from the same basic target of 150 to 300 minutes of activity per week (or 75 to 150 minutes if that activity is more intense). In other words, they’re essentially one-size-fits-all recommendations (which, in some ways, feels a bit like an exercise version of the Dietary Guidelines for Americans… which I have some thoughts on).

The problem is that physiology is about as far from one-size-fits-all as it gets. Genetics, environment, and lifestyle constraints all shape what someone can do (and what they should do), especially when the goal is reducing disease risk.

That’s why this new study caught my attention.2 It tried to tackle the idea of personalized physical activity prescriptions for reducing cardiovascular disease risk, based on a person’s predisposition, or baseline risk, for developing cardiovascular disease. The authors even put some specific numbers and thresholds on total activity, duration, and intensity, which is helpful. But what stood out to me even more was the broader theme that cardiovascular risk should probably be part of the conversation when we’re telling people how to move and train.

The study drew on data from 72,884 participants in the UK Biobank cohort. At the start, everyone wore an accelerometer (an activity-tracking device) for 7 days, giving researchers an objective look at how much moderate-to-vigorous, moderate-intensity, and vigorous-intensity physical activity people did, along with their sleep and sedentary time.

Researchers then followed participants for about 13 years, tracking cardiovascular disease events including heart disease, stroke, and cardiovascular-related mortality. But instead of simply comparing event rates across different activity levels (which has been done plenty of times before), the novelty was how they framed risk. Participants were first grouped based on how “at risk” they were for cardiovascular disease, using factors like age, total cholesterol and HDL cholesterol, blood pressure, and smoking status; as well as whether they had kidney disease, diabetes, or a family history of high cholesterol. From there, they were classified as low-to-moderate risk, high risk, or very high risk.

With those risk categories in place, physical activity was brought in to answer the study’s central question: “What are the optimal physical activity patterns to promote cardiovascular health among people with different levels of cardiovascular disease risk?”

More activity means lower risk (but it’s complicated)

Some of the initial findings were straightforward. Overall, higher levels of physical activity were associated with lower rates of cardiovascular disease across all risk groups in a dose-response manner.

Compared to people who got less than 150 minutes per week, those who achieved 150–300 minutes, 300–450 minutes, and >450 minutes per week had a 28%, 46%, and 58% lower risk of cardiovascular disease (more was better with no apparent upper limit). On the flip side, being sedentary for 7 or more hours per day was associated with a 23–28% greater cardiovascular disease risk (again, no surprise there).

Where things get more interesting is when you layer those relationships onto someone’s baseline risk. In the very high-risk group, additional activity appeared to “matter more” than it did in the low-to-moderate or high-risk groups. For very high-risk individuals, increasing weekly activity by 150 minutes per week was associated with a 20% reduction in cardiovascular disease risk—compared to “just” a 16% reduction for the lower-risk groups.

More moderate activity benefits high-risk groups

At this point, it would be easy to assume the takeaway is simply that more moderate-to-vigorous activity (and especially more vigorous activity) is always better, regardless of risk status. But that’s not exactly what the authors observed.

Instead, the benefits of increasing physical activity differed depending on where someone fell along the cardiovascular risk spectrum.

The authors walk through an example of someone “reallocating” 300 minutes of sedentary time to 300 minutes of moderate- or vigorous-intensity activity.

For someone at low-to-moderate cardiovascular disease risk, shifting 300 minutes toward moderate or vigorous activity (and away from sedentary time) reduced their 10-year risk of cardiovascular disease by 0.55% (moderate-intensity) and 0.85% (vigorous-intensity). That’s a small risk reduction, but remember these were already low-risk individuals.

In the high-risk group, the pattern flipped. Replacing sedentary time with 300 minutes of moderate-intensity activity reduced 10-year risk by 2.23%, while replacing it with vigorous activity reduced risk by only 1.47% (i.e., less than moderate-intensity activity). In the very high-risk group, the 10-year risk reduction from 300 minutes more of moderate activity was 4.36%, and for vigorous activity, it was 2.17%.

In short, the benefits of adding more vigorous-intensity activity were less pronounced than adding more moderate-intensity activity for individuals at higher cardiovascular disease risk, suggesting higher-risk individuals may benefit more from swapping sedentary time with moderate activity instead of vigorous (after a certain point, as we’ll see). For low-risk individuals, though, vigorous activity yielded a slightly greater risk reduction compared to the same duration of moderate activity.

The ideal “activity distribution”

The same general theme showed up when the researchers looked at the optimal mix of moderate and vigorous activity.

For low-to-moderate risk individuals, there didn’t seem to be an upper threshold for vigorous activity—spending a higher proportion of weekly activity time at vigorous intensity continued to yield additional benefits.

For high-risk individuals, however, risk reduction peaked when vigorous activity made up about 20% of their total weekly physical activity (about 60 minutes per week for someone engaging in 300 minutes per week). And for very high-risk individuals, the peak occurred when vigorous activity comprised about 14% of weekly physical activity (about 42 minutes per week). Beyond those points, the benefits of adding more vigorous activity started to decline. To be clear, risk never went up (above 0), but the benefits began to plateau or reverse when higher-risk individuals started adding more vigorous activity to their week. Moderate activity, on the other hand, appeared to have no plateau in terms of its benefits when replacing weekly sedentary time.

Whenever there’s a “surprising” finding—even (or perhaps especially) in observational research like the study we’ve been discussing—my first instinct is to ask: why? I think it helps to bring some applicability to what otherwise is just an association.

Why would someone at higher cardiovascular risk not derive the same (or even greater) benefit from adding more vigorous or high-intensity activity as someone at lower risk? On the surface, it feels counterintuitive.

To their credit, the authors did offer some speculation. They suggest that in high-risk individuals—who often already have elevated blood pressure and chronic inflammation—the more intense physiological stress of vigorous activity (on blood pressure, inflammatory molecules, and even hemodynamic stress within blood vessels) could potentially blunt some of its added benefits.

That could be true. But given the broader body of evidence, including randomized controlled trials showing benefits of high-intensity activity in people with preexisting conditions, I’m not fully sold that this is the main explanation.

Instead, I think this result may be less about vigorous activity having limited benefits per se and more about tradeoffs.

Because the substitution model the authors used holds total moderate-to-vigorous activity constant, meaning that “more vigorous” automatically means “less moderate.” And for higher-risk individuals, vigorous minutes may not represent the same physiological dose in the first place. What gets labeled as “vigorous” by an accelerometer could feel much closer to all-out effort for someone who’s less fit or has more comorbidities. Put those two together, and it’s easy to see how, beyond a certain point, swapping moderate minutes for vigorous minutes might stop adding extra protection.

There’s another, simpler possibility that moderate activity may already be doing so much heavy lifting in higher-risk people that the marginal gains from stepping up to vigorous intensity just aren’t as dramatic. When baseline risk is high, any consistent movement can substantially move the needle, so the jump from moderate to vigorous may look smaller than we’d expect.

Whatever the mechanism, one thing feels clear, and it has very little to do with the hazard ratios and modeling details that dominate papers like this.

Personalized activity guidelines are a no-brainer.

The idea that everyone benefits equally from 150–300 minutes of activity per week (or that 75–100 minutes of it should be intense) is insultingly simple. If your goal is reducing cardiovascular disease risk, you should likely move differently than someone whose primary goal is weight loss. And as this study highlights, baseline cardiovascular risk probably deserves a seat at the table when we think about how to distribute intensity across the week.

What’s somewhat serendipitous is that this study echoes a pattern we’ve seen for decades in endurance athletes—the “80/20” approach, where roughly ~80% of training is lower intensity and ~20% is higher-intensity work. In this study, the plateau for vigorous activity among high-risk individuals appeared right around that same 20% threshold.

Does that mean you should train like an athlete if your goal is to maximally reduce cardiovascular disease risk?

It might not hurt.

Thanks for reading. See you next Friday.

~Brady~

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• Equip Foods makes some of the cleanest, best-tasting protein products around. I am absolutely obsessed with their Prime grass-fed protein bars (the peanut butter ones are to die for). Take 35% off a subscription.

Greetings!

Welcome to the Physiology Friday newsletter.

Details about the sponsors of this newsletter and deals on products I love, including Ketone-IQ, Create creatine, Equip Foods, and ProBio Nutrition can be found at the end of the post.

When we work out, we get fitter. And we have a pretty good idea of how this happens (at least I hope so… otherwise those 8+ years I spent studying exercise science were for naught).

Endurance training causes well-characterized increases in the number and quality of our mitochondria (the body’s energy factories), builds a denser network of blood vessels and capillaries, makes us better at burning fat and carbs as fuel, and even changes the physical structure of the heart, which grows in size and strength to not only accommodate a greater volume of blood, but deliver that blood more effectively and forcefully to the rest of the body.

Strength training has some overlapping but distinct effects on physiology, mainly increasing the size and number of muscle fibers (referred to as hypertrophy and hyperplasia, respectively) as well as their strength and force output capacity; the latter a result of improvements in our neuromuscular system’s ability to coordinate and recruit contracting muscle fibers.

These improvements are largely attributed to peripheral signals from the body, that is, metabolic and mechanical changes occurring in our legs, arms, and elsewhere. In other words, exercise science has generally taken a “bottom-up” approach to training adaptation.

Myokines (chemical messengers originating from contracting muscle) and metabolites (byproducts of glucose/fat breakdown, such as lactate) released during exercise activate a range of signaling cascades that allow the body to adapt to the training stimulus (to prepare for that marathon you’ve got coming up in 12 weeks).

With all the focus on the body, we sometimes (ironically) forget about the brain, relegating it to the realm of psychologists and neuroscientists, not fitness experts. Of course, we know that exercise has brain health benefits in the short- and long-term. It’s probably one of the best things you can do to support healthy brain aging.

But what if the brain was actually responsible for the fitness we gain with exercise, perhaps getting stronger itself to facilitate endurance?

More intriguingly, could certain brain circuits be required for endurance adaptations?

According to a new study (it’s in the running for my favorite science finding of the year), the answer to both of those questions is yes. And even though the study was in mice, it might just change the way you think about your next workout.1

Researchers zeroed in on neurons in a region of the brain known as the ventromedial hypothalamus (VMH). Specifically, neurons in this region express a transcription factor known as steroidogenic factor-1 or SF1, and are considered to be a classic integrator hub for metabolic signals like glucose, insulin, and a hunger/satiety hormone called leptin in order to regulate energy expenditure.

In this paper, they referred to the VMH SF1 neurons as the candidate “exercise history encoder.”

They then built a tight and logically beautiful chain of evidence that goes like this:

1. Exercise activates these neurons in mice.

2. Training reshapes their post-run dynamics, and

3. The post-run activity of these neurons is required to get normal endurance adaptations.

Let’s take a closer look.

First, they asked, “Do these neurons even respond to exercise?”

After a single bout of treadmill exercise, SF1 neurons showed higher expression of BDNF (brain-derived neurotrophic factor), a gene that tends to rise when neurons are activated. In one VMH subregion, the proportion of SF1 neurons expressing BDNF increased from 34.5% to 42.2% with exercise.

Next was “Are these neurons necessary for endurance and training adaptation?”

They used a genetic trick to block neurotransmitter release from SF1 neurons—essentially cutting their output lines—then ran mice through a treadmill “stress test” (basically a VO₂ max test for mice) while measuring oxygen and CO₂ so they could infer fuel use. Importantly, VO₂max looked similar during the test, but endurance performance did not. The mice quit sooner and ran slower when SF1 neurons were blocked. Even more interesting, they shifted toward carbohydrate use earlier and at lower intensities, suggesting their fuel-selection strategy under stress was altered.

They also looked at muscle gene expression after training plus a run to exhaustion. In normal mice, exercise triggered lots of the expected transcriptional changes in muscle; in the SF1-silenced mice, those exercise-induced gene expression changes were “nearly abolished.” That’s a big deal, because those molecular shifts are part of how muscle becomes more oxidative and fatigue-resistant over time (i.e., how we build endurance).

Next question: “Does training change how these neurons fire?”

To answer this, they recorded SF1 neuron activity in living mice and found two main patterns: some SF1 neurons were suppressed right when the run ended (“post-run inhibited”), and others lit up when the run ended (“post-run activated”). After one week of treadmill training, the balance shifted strongly toward that post-run activation pattern. Roughly 31.8% of neurons were “post-run activated” on day 1 versus 53.2% on day 8 (with fewer neurons falling into the “inhibited” or “no change” buckets). Training made the brain respond more to the end of exercise, not less.

The final question is perhaps the most important one: “Is post-run activity of these neurons actually causing endurance gains?”

This is the part that makes the paper so fun. They used a technique called optogenetics to manipulate SF1 neurons after each training session—turning them off (inhibiting them) or turning them on (stimulating them).

When they inhibited SF1 neurons for 15 minutes after every workout, the normal gains in endurance were blunted. They also prevented the typical post-exercise rise in blood glucose (without changing body weight), hinting that part of the signal of endurance adaptation might be about restoring fuel availability for recovery.

When they stimulated SF1 neurons for 60 minutes after workouts, endurance improved beyond the usual plateau, and even stimulation alone (without training) nudged the mice’s metabolism toward higher carbohydrate use and higher overall energy expenditure/oxygen consumption. It had an “exercise mimicking” effect.

There was also evidence of training-related plasticity in this brain circuit. SF1 neurons became more excitable and appeared to receive stronger excitatory input after endurance training, which fits the idea that the brain is literally encoding exercise history.

This was one of those studies I simply couldn’t wait to read and write about. It doesn’t have a clean, actionable takeaway like some of the other physiology studies I share (I’ll try to give you some), but it’s freaking fascinating (to me, and hopefully to you).

It argues for a meaningful top-down component to exercise adaptation by telling us that, at least in mice, the brain is largely responsible for the downstream recovery and remodeling programs after exercise, especially in the window right after you stop. In fact, this paper strengthens the idea of a “narrow” post-exercise recovery window during which a training session is encoded, and future adaptations are hardwired into our body.

Does this mean we should start prioritizing “neurological recovery” techniques after our workouts as we do with nutrition and muscle-focused approaches? Perhaps. It tells me that maybe we shouldn’t finish a workout and then hop immediately into high-stress work or stimulating social media. Our brain and nervous system need time to do their job.

The obvious caveat here is translation. These are mice, on treadmills, with genetically/optogenetically manipulated neurons. That’s not the same as saying we’ve found the “endurance switch” in humans.

Ok, now bear with me while I get science-fictiony. Because what immediately came to mind after reading this study was two scenarios where these findings might apply: an “exercise pill” and neurodoping.

In the world this study hints at, the most powerful performance enhancer wouldn’t be something you take before a race; it would be something that quietly tweaks the 15–60 minutes after the workout, when the body is deciding what to rebuild, what to store, and what to upregulate for next time. You don’t need to run harder. You just need to convince the system that you did, and that it should adapt accordingly.

This is where the idea of “neurodoping” gets enticing (and a little creepy).

If endurance adaptation is partly gated by a brain circuit that can be turned up or down, it opens the possibility that neuromodulation could make training “count” more. Think recovery-enhancing brain stimulation, wearable brain tech, or a pill that amplifies the brain’s post-exercise learning signal. It’s also where the concept of “exercise in a pill” becomes increasingly possible.

In the past, most “exercise-mimicking drugs” (or the idea of them) have targeted a single pathway. But if we could design something to target the brain’s key endurance-enhancing command center, it might be able to integrate all relevant training signals to recapitulate the benefits of a workout without a drop of sweat lost. It could be a game-changer for people with diseases, older individuals with limitations to exercise, injuries, or those days when you just can’t find the time for the treadmill.

I’ll be a bit more convinced of these scenarios if the findings of this study can be replicated in humans. Until then, let’s just embrace the idea that there is potentially still a LOT to learn about how the body rewires itself to perform better.

And the next time you set a new personal best in a 5k or a marathon, thank your brain as much as you do your body for getting you to the finish line.

Thanks for reading. See you next Friday.

~Brady~

Ketone-IQ is high-performance energy in a bottle. I use it for post-exercise recovery along with enhancing focus, mood, and cognition. Take 30% off your order.

Create is the first “modern creatine” brand. They sell a wide range of creatine monohydrate gummies—and yeah, their gummies actually contain creatine, unlike some other brands. They’re giving my audience 20% off their order.

ProBio Nutrition—the all-in-one supplement that I use every single day—is offering 20% off. My preference is the tangy orange flavor, but they also sell an unflavored “smoothie booster” that’s great in a shake, smoothie, or juice.

• Equip Foods makes some of the cleanest, best-tasting protein products around. I am absolutely obsessed with their Prime grass-fed protein bars (the peanut butter ones are to die for). Take 35% off a subscription.

Greetings!

Welcome to the Physiology Friday newsletter.

Details about the sponsors of this newsletter and deals on products I love, including Ketone-IQ, Create creatine, Equip Foods, and ProBio Nutrition can be found at the end of the post.

For this week’s newsletter, I wanted to share some summaries of the most interesting studies that I stumbled upon in the last month related to endurance exercise. Most of these first appeared in my other newsletter (Run Long, Run Healthy). I cover:

• Data that higher training volumes increase coronary atherosclerosis risk

• Money ($) as a performance enhancer

• If pre-run whey protein causes GI distress

• Why sodium bicarb (baking soda) isn’t that effective

• How good are smart watches for measuring sweat rates?

Enjoy!

High-Volume Training Raises Atherosclerosis Risk

There’s a persistent finding in sports cardiology that makes runners (including me) uneasy. Middle-aged and older endurance athletes can show more coronary plaque and higher coronary calcium than similarly healthy non-runners.

A big debate has been why. Is it intensity? Volume? Lifetime exposure?

A new publication from the Master@Heart study tries to tighten that up by using wearable-derived training load.

This was an observational, cross-sectional analysis of 222 men (age 54): 77 lifelong athletes, 98 late-onset athletes, and 47 physically active controls. Training load was quantified in two ways:

1. Objective wearable-derived training over 12 consecutive months, including duration and heart-rate–weighted intensity. They used eTRIMP (a training impulse metric: duration multiplied by intensity weighting across heart rate zones).

2. Self-reported training measures.

Then everyone underwent coronary CT angiography (CCTA—a clinical imaging procedure used to measure levels of plaque and calcium in the coronary arteries of the heart). The main markers included the presence of ≥1 plaque, coronary artery calcification (CAC > 0), and more clinically concerning calcification (CAC > 100). They compared coronary disease prevalence across training-loads.

The objective wearable data produced a clear gradient: higher objective training load was associated with a substantially higher prevalence of subclinical coronary disease.

Compared with the lowest training load, athletes with the highest training load had a 485% increase in the odds of having ≥1 plaque (and the raw prevalence was 74.5% vs 36.4%); a 403% higher odds of a CAC > 0; and a 250% greater odds of having a CAC > 100.

They also looked at objective training duration. The lowest level was 0.5–3.3 hours/week, and the highest was 9.2–26.5 hours/week. Again, those with more training hours had higher plaque prevalence (70.9% vs 40.0%) and higher odds of plaque and calcified plaque features.

One of the most interesting (and practically relevant) findings was that time spent in higher-intensity zones, by itself, was not clearly associated with coronary disease when separated from duration. The signal was strongest for high duration and for the combined load captured by eTRIMP (which essentially blends duration with cumulative intensity exposure).

This paper also highlights a measurement issue. Self-reported training duration was much weaker as a predictor. Self-reported training load was not meaningfully associated with coronary disease, which supports the idea that wearable-derived metrics may be necessary if we’re going to understand the dose–response relationship between training and coronary plaque.

Practical takeaway

For masters athletes, the headline isn’t “exercise causes heart disease”—it’s that very high weekly training duration (especially when paired with high cumulative load) may be linked to a higher prevalence of coronary plaque and coronary calcium, even in men with otherwise low traditional risk. Treat cardiovascular risk like something you manage alongside performance: know your blood pressure and lipids, don’t ignore family history, and consider a more medicalized check-in if you’re doing consistently high-volume training year after year. And if you’re trying to interpret your own risk, the study also reinforces that objective data (hours, load, intensity distribution) is what seems to map onto the coronary findings, not how much you “think” you train.

Can Money Enhance Your Performance?

Most of us like to think we’re tough because we’re tough. Not because someone dangled a gift card in front of us.

But all of us have run a hard workout better when we had a race on the calendar, a training partner next to us, or even a Strava segment we really wanted. Motivation is part of the engine. But does motivation change fatigue itself, or just how long we’re willing to tolerate the exact same level of exertion?

A new study went straight at that question. A total of 18 healthy young adults (9 women, 9 men; ~23 years old) completed three lab visits. First, they did a test to determine VO₂max and peak power, plus estimate the maximal metabolic steady state (MMSS). Then they completed two identical experimental sessions:

1. A heavy-intensity cycling time-to-task failure at 95% of estimated MMSS—so hard, but theoretically sustainable for a while.

2. After 1 minute of rest, they immediately did an extreme-intensity cycling time-to-task failure at 90% of peak power—short, brutal, and heavily dependent on what you have left in the tank.

In the second experimental session, participants were kept blinded until they reached the moment when they indicated they were about to stop. At that point, researchers offered them an unexpected monetary reward for continuing: one raffle ticket per additional minute (for a draw to win $250 CAD) and $10 CAD for every additional 5 minutes they kept going.

The incentive did exactly what you’d predict. It didn’t change when people thought they were one minute from quitting, but once money entered the chat, people extended the suffering. Heavy time-to-task failure increased from 46 minutes on average to 53 minutes—roughly a 15% longer ride on average. But the tradeoff showed up immediately in the second test: extreme time-to-task failure dropped from 68 seconds to 57 seconds—about a 16% reduction.

So the reward bought more minutes in the long, heavy domain… but it cost performance in the follow-up all-out effort. That’s a pretty neat demonstration of the pacing/energetic reality underneath “motivation.” There’s a trade-off.

Now the most important part. The authors didn’t just record time. They measured neuromuscular fatigue in the quad muscles of the leg to test whether the muscles or the brain were the true source of the urge to quit.

Across both conditions, the heavy cycling bout produced a big drop in neuromuscular function: maximal force output fell by about 31%. Crucially, those declines were not worse in the reward condition despite the longer time spent cycling. The fatigue profile after the heavy bout looked essentially the same whether people stopped at 46 minutes (without the reward) or pushed to 53 minutes (with the reward).

Furthermore, the reward didn’t meaningfully alter the perceptual profiles (fatigue, pain, effort). People just kept going longer with the same internal experience and a similar neuromuscular endpoint.

Practical takeaway

Physiology sets the stage, but motivation can meaningfully shift how long you’ll operate near your limits. The practical play here is to use motivation strategically—training partners, structured goals, external accountability—when you want to extend tolerance in long threshold-style work, while recognizing the cost. Squeezing out extra minutes today can blunt the quality of the next hard effort if you’re stacking sessions.

Are We Overthinking Pre-Run Protein?

Most runners have internalized (and stressed about) a simple rule that protein before running = GI disaster. So we play it safe with a bagel, banana, maybe a little jam, and save protein for after. Which is fine… until you realize that a lot of training happens in the real world, where you might be squeezing in a moderate-hard run an hour after breakfast, or you’re trying to support recovery without constantly shoving down carbs.

A new study set out to test whether a “moderate” protein intake before a workout actually makes your stomach revolt compared to a low-protein version.

The researchers ran a study with 13 recreational competitive runners (8 women, 5 men) who were selected specifically because they reported at least two chronic exercise-associated GI symptoms at baseline.

Each runner completed two test days separated by a 1–5 week washout. They arrived fasted (10+ hours) for morning testing and drank one of two shakes 1 hour before running: a low-protein whey shake (0.15 g/kg) or a moderate-protein whey shake (0.4 g/kg). Both shakes included roughly the same carbohydrate dose (0.75 g/kg) mixed with water.

For context, using their average body mass of ~65 kg, the low-protein shake worked out to ~9 g protein, and the moderate-protein shake to ~23 g protein, with carbs ~50 g in both shakes. Calories were higher in the moderate-protein condition (~299 kcal vs ~234 kcal) because protein adds energy, and they didn’t reduce carbs to compensate.

The run was performed on a treadmill—10 km at 85% of the runners’ 10K race pace (so not race intensity, but definitely not easy). They also did a standardized warmup and cooldown.

GI symptoms happened… regardless of protein dose. Even though this was “only” a ~1-hour treadmill 10K effort, GI symptoms were extremely common. During the run, 12/13 runners reported at least one symptom in the low-protein condition, and 11/13 in the moderate-protein condition. The number of total symptoms increased from fasting to running in both trials, but the protein dose didn’t meaningfully change the total symptom burden. The most common symptoms during the run were things like belching, fullness/heaviness, side stitches, flatulence, urge to defecate/urinate… basically the runner’s GI starter pack.

The only clear protein-related difference was bloating severity. When they scored symptom severity (0–9 scale), almost everything looked similar between conditions… except bloating. During the run, bloating severity averaged 0.54 in the low-protein trial vs 1.23 in the moderate-protein trial.

Gut fullness rose after the shake, peaked around 30 minutes, and didn’t differ much between shakes. Statistically, the two conditions weren’t different—but practically, there was a subtle pattern that in the moderate-protein condition, fullness stayed above fasting levels through all time points, including post-run, suggesting protein (or extra calories) may leave that fullness sensation hanging around longer (for better or worse).

Importantly, perceived exertion didn’t change at all—the runners didn’t feel that the run was harder with more protein.

Practical takeaway

If you’ve been avoiding any meaningful pre-run protein out of fear of GI issues, this study should make you a little less anxious about it (at least for whey protein in a shake) if taken an hour before a moderate-hard ~10K effort. Up to 0.4 g/kg (roughly 20–25 g for many runners) didn’t blow up overall GI symptoms, fullness, blood sugar, or how hard the run felt, though it did increase bloating a bit, so it’s not a free-for-all if you’re prone to this symptom. If you want more protein before certain workouts (because it fits your schedule or helps you hit daily targets), trial it on controlled training days first, and if you’re already a GI-sensitive runner, don’t assume you need to zero out protein—start modest, keep the food form easy to digest, and let tolerance dictate your routine.

Can Baking Soda Boost Your Workout?

Sodium bicarbonate is one of those supplements that sounds too good to be true—like you can outsmart fatigue with a simple chemistry trick. Load up on baking soda, buffer lactate production during high-intensity exercise, and perform better. The problem is that running isn’t a lab bench… and bicarbonate has a not-so-minor side effect profile that most runners learn about the hard way.

A new systematic review and meta-analysis set out to answer a very specific (and very runner-relevant) question: Does a single oral dose of sodium bicarbonate actually improve continuous running performance?

The authors searched major databases for randomized, double-blind, placebo-controlled trials and only included studies that tested continuous running performance after one oral dose of sodium bicarbonate. They excluded multi-day loading protocols and excluded repeated-sprint/interval running designs, which helps keep the question specific to a single, hard, continuous run.

They ended up with 11 studies totaling 126 participants. Most subjects were men (84% male), generally young (roughly early 20s), and mostly trained/competitive runners. The dosing was typically 0.3 g/kg body mass (range 0.2–0.4 g/kg), usually taken ~90 minutes before the performance test. Performance tests ranged from 1 to 30 minutes and included fixed-distance time trials (like 400, 800, and 1500 meter runs), time-to-exhaustion protocols, a 30-minute maximal distance test, and a graded test.

Across studies that reported symptoms, GI issues occurred in 29.5% of bicarbonate trials versus 2.6% with placebo. And GI-related withdrawal (i.e., people who couldn’t complete the protocol) happened in 8.7% of bicarbonate conditions versus 1.6% with placebo. The most common complaints were diarrhea (9.0%), unspecified GI upset (9.0%), nausea/vomiting (6.4%), and stomachache (5.1%).

On performance tests, bicarbonate produced a small improvement overall. After adjusting for GI-related withdrawals (excluding people who “dropped out”), effects were still small. They also explored who might benefit more. Across studies, a higher proportion of male participants and a higher average body mass were associated with larger effects.

Practical takeaways

In the best-case scenario, the average benefit for sodium bicarbonate on continuous running performance looks tiny once you correct for the ways this literature tends to overstate effects, while the GI risk is very real (roughly a 1-in-3 chance of symptoms and close to 1-in-11 odds of not tolerating it at all). If you’re a larger male runner targeting shorter, very hard events (think a few minutes to ~15 minutes), you might be in the subgroup where it’s more likely to help, but even then you should treat it like any other high-risk strategy: test it in training, control the timing (~60–150 minutes before), don’t debut it on race day, and be honest about whether a small potential upside is worth the real chance of GI distress.

Can Your Watch Predict Your Sweat Rate?

I love wearables, but I’m also deeply suspicious of them, especially when they start spitting out numbers that feel medical (recovery scores, “training readiness,” and now… sweat loss). Sweat loss is one of those deceptively simple variables that’s hard to estimate without doing something unsexy like stepping on a scale before and after a run.

A new study asked if smartwatch sweat-loss estimates are accurate enough to guide hydration the way we’re starting to use them.

Researchers recruited 111 physically active adults (average age ~40; average VO₂max ~46.8 ml/kg/min) and had them complete up to four running visits. Everyone did an outdoor 5K and an indoor 5K, and then a third run that varied (2.5K, 10K, 20K, or an interval workout). In total, they collected 293 running trials, with 291 producing sweat-loss estimates from both watches.

Participants wore two popular devices at the same time: the Samsung Galaxy Watch5 and the Garmin Forerunner 945. The “gold standard” comparison was the change in nude body mass pre- vs post-run, converted to fluid loss, with strict rules about limiting fluid intake and bathroom use outside the exercise window.

Both devices tracked the general direction of sweat loss, but missed the actual amount by a lot.

Across all conditions, average sweat loss was ~659 mL, with a huge range (80 mL to 3,300 mL). Body mass loss ranged from 0.25% to 2.73%.

The correlation looked decent, especially for Samsung (0.90) and still “okay” for Garmin (0.77), meaning the watch’s estimations were generally in line with the direction and magnitude of actual sweat loss. But the error for the devices was high: Samsung’s average absolute percentage error was ~25%, and Garmin’s was ~33%. In plain terms: if you truly lost 1000 mL, your watch might reasonably tell you you lost ~670 mL… or ~1330 mL!

The absolute errors reinforce that point: average miss was ~151 mL for Samsung and ~218 mL for Garmin, but the more important detail is the spread. Samsung’s differences ranged roughly from ~428 mL under to ~491 mL over; Garmin ranged from ~464 mL under to ~727 mL over.

They also split the data by distance (short ≤5K, long ≥10K, and intervals) and environment (indoor vs outdoor). The headline didn’t change here, and the error stayed stubbornly high across conditions.

Practical takeaway

The authors’ bottom line is the one I agree with: these watches may be useful for trend tracking (am I generally sweating more this summer than in winter?), but they’re not precise enough to replace weigh-ins when accuracy matters. If you’re using sweat-loss numbers to guide a real hydration plan—especially for long runs, marathons, or hot conditions—treat your watch’s “sweat lost” as a rough guess, not a dosing chart. The average error here is big enough to push you toward underdrinking or overdrinking depending on the day. The better play is to do a few simple pre/post-run weigh-ins (same clothes, towel off sweat, track what you drank) to learn your personal sweat rate, then use the watch as a consistency tool to notice patterns (heat, humidity, pace, duration) rather than as the authority. Your scale is still the gold standard!

Thanks for reading. See you next Friday.

~Brady~

Ketone-IQ is high-performance energy in a bottle. I use it for post-exercise recovery along with enhancing focus, mood, and cognition. Take 30% off your order.

Create is the first “modern creatine” brand. They sell a wide range of creatine monohydrate gummies—and yeah, their gummies actually contain creatine, unlike some other brands. They’re giving my audience 20% off their order.

ProBio Nutrition—the all-in-one supplement that I use every single day—is offering 20% off. My preference is the tangy orange flavor, but they also sell an unflavored “smoothie booster” that’s great in a shake, smoothie, or juice.

• Equip Foods makes some of the cleanest, best-tasting protein products around. I am absolutely obsessed with their Prime grass-fed protein bars (the peanut butter ones are to die for). Take up to 10% off site-wide and up to 35% off a subscription.

Greetings!

Welcome to the Physiology Friday newsletter.

Details about the sponsors of this newsletter and deals on products, including Ketone-IQ, Create creatine, Equip Foods, and ProBio Nutrition can be found at the end of the post.

“Overtraining” is a vague term, and that’s an understatement.

Nobody in exercise science really knows how to define it. And we don’t really have any biomarkers to diagnose it (that’s not for a lack of trying).

Sure, we can feel fatigued, chronically sore, or lack motivation to train. These correlate well with training load. A poor capacity to adapt (you’re not getting better with each workout) is also one of the more well-accepted markers of overtraining/overreaching (as it’s more commonly referred to). But ask two different coaches what overtraining is, and you’ll probably get two completely different answers.

We lack a precise characterization of overtraining for several reasons, but one of the main ones is that we just don’t have a good model of how to “overtrain” people. What’s the minimum amount of hard exercise someone needs to do before they hit their breaking point? And how can you implement this into a study? On the other hand, maybe it’s all about a lack of recovery rather than too much training per se.

Without the ability to subject people to the right overtraining stimulus, it’s impossible to define the “signature” of an overtrained athlete.

That’s what makes today’s study so interesting—it uses what is perhaps the most demanding athletic endeavor—the Tour de France—and uses it as a kind of physiological stress test for human metabolism.1

Cyclists competing in the Tour de France are operating at (or above) the upper limits of sustained human metabolic energy expenditure—they’re burning nearly 4–5 times their basal metabolic rates (upwards of 8–10 thousand calories per day) for three weeks. During the Tour, athletes cover 3,404 kilometers (2,115 miles) with more than 56,000 meters of climbing across 19 days (including 2 blissful rest days).

If we want a model of excessive exercise (and diminished recovery), the Tour de France is it.

By characterizing the body’s response to this grueling event, researchers hoped to uncover a “metabolite signature” of excessive exercise and, hopefully, in the process, discover novel biomarkers of overtraining that could be used to diagnose when elite athletes (and perhaps the rest of us, too) are getting too close to the edge.

The researchers followed 7 professional male cyclists during the 2023 Tour de France (one rider crashed and was subsequently excluded), collecting fasting blood samples in the morning at four time points: the start of the race, on rest day 1 (day 10 of the Tour), on rest day 2 (day 17 of the Tour), and the finish.

What they were mainly looking at in this study was blood metabolites—small molecules circulating in the blood that come from normal chemical reactions in the body. They’re the “chemical fingerprints” of what the body is doing and include things like fuel molecules (glucose, fatty acids, ketones), amino acids, byproducts of energy production (lactate, citrate), and signals and stress markers tied to inflammation and oxidative stress. Measuring a wide range of metabolites is basically taking a snapshot of the body’s metabolic state—how you’re producing energy, what you’re burning, what you’re conserving, and what systems might be under strain.

The Tour causes a broad depletion of metabolites

Across the Tour, 118 metabolites (43% of those measured) significantly changed, and the dominant direction of the change was down. When comparing the start of the race to the finish, far more metabolites decreased than increased, and the biggest change occurred from the start to the first rest day. The first 7–10 days were characterized by a huge metabolite shift with the general theme of utilization outpacing replenishment (hence why so many metabolites went down and not up).

This tells us that:

1. The body is most “dysregulated” at the initial shock of the Tour de France and

2. There’s somewhat of an “adaptation” after about a week of prolonged racing, likely when the body (somewhat) starts to drift back down (or more likely get body-slammed) into homeostasis.

So, what changed the most?

Two pathways stood out:

• Beta-oxidation (the pathway through which we “burn fat”)

• Glutathione metabolism (our body’s main antioxidant pathway)

A large part of the signature was tied to fatty acid metabolism, in particular carnitine and acylcarnitine, which are part of the shuttle system that helps move fatty acids into the mitochondria to produce energy. Free L-carnitine and several medium- and long-chain acylcarnitines went down, while at the same time, intermediates of the Krebs cycle (aka the TCA cycle, our body’s main energy-production pathway) increased over time, indicating a heavy reliance on aerobic metabolism throughout the Tour (no surprise there).

Normally, a downregulated acylcarnitine pool could be interpreted as reduced fat oxidation. But the authors argue that the stability of the dominant pools (major fatty acids and the most abundant acylcarnitines), plus rising TCA intermediates, suggests fat oxidation was not globally reduced. Instead, the pattern looks like selective adaptations in transport/oxidation capacity—potentially reflecting increased tissue uptake of carnitine to sustain mitochondrial fatty acid transport under extreme demand.

They also outline plausible mechanisms for why L-carnitine and specific acylcarnitine pools might fall: reduced biosynthesis, reduced activity of transporters, or enhanced utilization.

One of the coolest details here was the selective depletion of saturated (but not unsaturated) fatty acids, which diverges from the “typical” response where unsaturated fats are preferentially oxidized during exercise. The authors propose a metabolic-efficiency explanation: saturated fat oxidation requires fewer steps to “burn” than unsaturated fats of the same chain length, potentially making it the “cheaper” substrate under conditions of metabolic overload (like the race).

The results also highlight cysteine as the most depleted semi-essential amino acid, showing a continuous decline across all cyclists. They connect this to increased production of reactive oxygen species (ROS). How’s this tied to glutathione? Remember that glutathione is the primary intracellular antioxidant. Because cysteine is the rate-limiting substrate for glutathione synthesis, progressive cysteine depletion may reflect heightened demand for glutathione production to buffer oxidative stress.

Enter a practical application for overtraining. N-acetylcysteine (NAC) has been shown to improve glutathione status and endurance performance in fatigued states, and could plausibly help by supporting antioxidant capacity.

Intuitively, these two major shifts make a lot of sense if we simply think about the demands that the Tour de France imposes on the body.

While it involves a lot of intense cycling, most of the race involves steady state, aerobic riding, which places a huge stress on the body’s fat metabolism. At the same time, the demands of the event coupled with the need for recovery stress endogenous antioxidant systems (glutathione) so much that it simply cannot keep up. And these two systems were the main ones that stood out as being the most dysregulated after three weeks of racing.

What shows up as “overtraining biomarkers?”

The fatigue analysis is one of the most practically useful parts of the paper.

Researchers didn’t just measure metabolites, they asked if any of these track with how destroyed the athletes feel. The goal was to identify candidate blood-based biomarkers for training monitoring and detecting training imbalances.

Cyclists rated perceived fatigue on a 0–10 scale at each blood draw, rising from 0.5 at the start to 5 at the first rest day, and reaching 8 by the finish.

Then they correlated fatigue with every metabolite, identifying 84 significant metabolite–fatigue correlations, and 77 were negative (fatigue up, metabolite down). These metabolites spanned lipids, fatty acids, and amino acids—and importantly, they note that most of these haven’t previously been linked to fatigue/overreaching, which is why they pitch them as “novel” biomarker candidates.

The strongest negative correlations were lysophosphatidylcholine (LysoPC, a derivative of phosphatidylcholine) and sphinganine. Both sit in the world of cell membrane metabolism, prompting the authors to suggest that cell membrane dynamics may play a role in fatigue development.

Only seven metabolites were positively correlated with fatigue, including citrate and fumarate, consistent with increasing TCA cycle activity (more aerobic metabolism) alongside rising perceived fatigue. Makes sense to me.

Along with just being a truly interesting and one-of-a-kind study in an elite subpopulation of athletes, this paper provides a ranked list of candidate signals that move in lockstep with perceived fatigue under extreme real-world training load. These signals are tied to cell membrane biology, mitochondrial throughput, and a broader theme of metabolite depletion outpacing replenishment.

The authors position these as potential biomarkers for training monitoring—not “final answers,” but a set of hypotheses now testable in larger athlete cohorts and in settings where some athletes do tip into non-functional overreaching or overtraining syndrome.

Was the novel finding that three weeks of intense bike racing places an enormous stress on the human body? No, the novel finding was how, and how that expressed itself in the body’s metabolic signature.

Because most of us aren’t regularly measuring our blood for a detailed metabolomic analysis (I wish), we don’t really have a way of using biomarkers like these in our own training.

But that’s why I think the correlation with fatigue in this study is so potentially useful. What if it turns out that your perceived effort actually tracks quite nicely with these blood-based biomarkers? If that’s the case, our simple ratings of fatigue every day or throughout a training cycle may be all that we need to hint at whether we are veering into “overtraining territory.”

It’s a friendly reminder that in-depth analyses are fun, but how you feel is ultimately the only health and training metric that really matters. That’s true whether you’re a Tour de France cyclist or somebody like me who rides endless hours on a bike in my garage, going nowhere.

Thanks for reading. See you next Friday.

~Brady~

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Create is the first “modern creatine” brand. They sell a wide range of creatine monohydrate gummies—and yeah, their gummies actually contain creatine, unlike some other brands. They’re giving my audience 20% off their order.

ProBio Nutrition—the all-in-one supplement that I use every single day—is offering 20% off. My preference is the tangy orange flavor, but they also sell an unflavored “smoothie booster” that’s great in a shake, smoothie, or juice.

• Equip Foods makes some of the cleanest, best-tasting protein products around. I am absolutely obsessed with their Prime grass-fed protein bars (the peanut butter ones are to die for). Take up to 10% off site-wide and up to 35% off a subscription.

I’ve been using exogenous ketones for several years now, and Ketone-IQ has become the brand that I trust and use almost daily for workout recovery, performance, and cognitive enhancement. And I’m excited to announce that I just joined their scientific advisory board and can’t wait to help share the science of ketones with the world.

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The article I’m reacting to: Can You Eat Too Much Protein? 5 Ways to Tell”

The author points to protein being added to everything now: high-protein pasta, protein lattes, protein popcorn, etc. Then it claims the “latest dietary guidelines” dramatically increased protein targets to ~1.2–1.6 g/kg/day (and gives an example of ~80–110 g/day for a 150-lb person).

The article quotes an expert saying the “vast majority” already get enough. I’m skeptical of that blanket statement.

The “5 warning signs” — and my responses

• 1) Dehydration

  • Article claim: extra protein makes kidneys “work overtime,” nitrogen must be excreted, urination increases, and this can lead to dehydration.

  • My take:

    • Yes, protein metabolism produces nitrogenous waste, but the “kidneys working overtime” language is dramatic.

    • Even if urination increases somewhat, the solution is straightforward: drink more.

    • People are not walking around chronically dehydrated because they eat “too much protein.”

    • The symptom list is comically obvious (thirsty, tired, dizzy… groundbreaking).

• 2) Foamy/bubbly urine (proteinuria)

  • Article claim: foamy urine can signal proteinuria; “some studies” suggest a link between high protein intake and proteinuria.

  • My take:

    • The “link” is presented like a shocking revelation: eat more protein → excrete more protein. Of course that can happen.

    • The article uses medical jargon in a way that feels designed to alarm people.

    • It also claims frequent urination can be another symptom of proteinuria and “too much protein.”

    • My reaction: the logic gets messy—are you dehydrated or peeing all the time? Pick a lane.

• 3) Kidney stones

  • Article claim: excess protein (especially animal protein) is something to watch for, and it suggests mechanisms related to urine acidity/oxalates.

  • My take:

    • Notice how they always target animal protein—as if plant protein is immune from “overdoing it.”

    • I’m not anti-plant-based at all; I eat plenty of plant foods. But this “animal protein is bad” bias shows up constantly.

• 4) Irregular bowel movements/constipation

  • Article claim: too much protein can cause constipation because people reduce fiber when they increase protein.

  • My take:

    • I’m not an expert here, but I don’t buy the simplistic “you must eat fiber to poop” narrative.

    • There are people eating animal-based/carnivore diets who still have bowel movements.

    • There’s also evidence suggesting that for some people, reducing fiber can improve bowel regularity.

    • The article assumes that everyone increasing protein is automatically cutting out other nutrients and eating an unbalanced diet, which is a weak assumption.

• 5) Weight gain

  • Article claim: there’s a lack of evidence that high-protein diets cause weight loss; some people gain weight.

  • My take:

    • That “lack of evidence” statement is false—there’s plenty of evidence that higher-protein diets can support fat loss and improve body composition compared to lower-protein diets.

    • The article then backtracks into the obvious: if you eat more total calories (even from protein), you gain weight.

    • Important nuance the article misses: if extra calories come from protein, it often has less impact on weight gain than extra calories from fat/carbs due to the thermic effect of food (protein costs more energy to digest).

• The article’s “how to eat a balanced amount of protein” section

  • It claims most people eat more protein than they need, then cites examples like:

    • ~17 g protein in a small cod serving

    • ~26 g protein in a small chicken breast portion

  • My reaction:

    • Those portions are small, and the protein amounts are not necessarily enough for a full meal.

    • For many people, a more useful target is ~25–30 g minimum per meal (often more depending on body size, training, and goals).

• Mediterranean diet commentary

  • The article recommends a Mediterranean-style pattern and suggests ~15% of calories from protein.

  • My reaction:

    • I don’t disagree that Mediterranean-style patterns can be healthy.

    • But “the Mediterranean diet” is treated like one fixed thing—it’s really a broad pattern, and that term has started to annoy me.

    • It’s basically describing a plant-forward pattern with fish/poultry and other whole foods.

• “Some people may need more protein” (surgery, elite athletes, aging)

  • The article says certain groups may benefit from higher protein, otherwise talk to a professional before increasing protein.

  • My reaction:

    • Sure, talk to a professional if you have a medical condition.

    • But the broader cultural push to warn people away from protein is bizarre—most people are not “overdoing it.”

• The article’s takeaways (and my overall verdict)

  • The article concludes that too much protein can stress the kidneys and cause symptoms like frequent urination/constipation and possibly kidney stones, and advises reducing portions and adding fruits/vegetables/whole grains.

  • My final take:

    • Not the worst thing I’ve ever read, but it’s still loaded with fear-mongering and outdated framing.

    • The biggest issue: no citations—just quotes from people who sound years behind the current state of nutrition research.

    • We should be encouraging adequate protein intake, not trying to scare people into eating less.

Greetings!

Welcome to the Physiology Friday newsletter.

Details about the sponsors of this newsletter and deals on products, including Ketone-IQ, Create creatine, Equip Foods, and ProBio Nutrition can be found at the end of the post.

Exercise is medicine. But like all medicines, the dose makes the poison.

Can “too much exercise” send you to an early grave?

That argument has been around for decades, but the scientific story here is complicated. At a population level, regular exercise is overwhelmingly protective for cardiovascular disease and mortality. But when you zoom in on very high lifetime training volumes—especially in older men—the literature starts to show a more mixed picture.

The best-established example is atrial fibrillation (AFib for short). Long-term endurance athletes, particularly male and master athletes, show higher AFib rates than sedentary peers. That’s likely due to a mix of structural remodeling (bigger chambers of the heart known as the atria), autonomic shifts (more parasympathetic tone), and repeated inflammation/strain from intense exercise and racing built up over years and decades. While it’s concerning because it can raise stroke risk and make you feel lousy or limit performance, in endurance athletes, AFib is usually manageable and treatable.

Ventricular arrhythmias (abnormal heart rhythms originating in the larger chambers of the heart known as the ventricles) are a different beast: they’re less common, potentially more serious, and much harder to predict. The debate in sports cardiology is whether endurance exercise can create a ventricular arrhythmia problem (by causing scarring or ventricular remodeling), or whether exercise mostly reveals an existing vulnerability (a subtle heart abnormality, a genetic predisposition, or a pre-existing scar) by adding a potent trigger.

But in real life, endurance athletes often look normal on routine screens, and the arrhythmias can be intermittent. Which raises a practical question: is there something about how we train—volume, intensity, pattern—that meaningfully changes risk? Or is this mostly about who you are and what your heart tissue looks like?

A new study tries to answer that with unusually granular data and very rigorous measurement choices.1 It’s probably the most in-depth characterization of the training-arrhythmia risk relationship I’ve seen, and the conclusions (I think) are incredibly important.

Researchers recruited 106 “healthy” veteran male endurance athletes (cyclists/triathletes), all >50 years old, with a long history of heavy training (>10 hours/week for >15 years). Importantly, they excluded anyone with known cardiovascular disease and athletes with symptoms that might hint at disease (chest pain, palpitations, shortness of breath, or fainting).

Everyone underwent a clinical exam plus a 12-lead ECG (heart rhythm monitoring), cardiac magnetic resonance imaging (MRI) to detect scarring and fibrosis of the heart, and a supervised maximal exercise test to measure maximum heart rate, performance variables, and anchor their training load.

The training load measurement is where the study gets detailed. Athletes used computerized exercise tracking devices and synced their workouts to an online diary (TrainingPeaks). Training wasn’t just volume; they quantified intensity in multiple ways using frequency, duration, and distance from the recorded sessions. They also calculated Training Stress Score (TSS) as their primary training load metric and broke intensity into time-in-zones (Z1–Z6), using standardized zone cutoffs relative to each athlete’s physiology.

This lets them ask a more precise question than how much athletes trained. They can ask about training intensity distribution to figure out if, say, spending more time in zone 2 or doing more threshold intervals raises one’s risk of a ventricular arrhythmia.

Every participant received an implantable loop recorder—a small device placed under each athlete’s chest skin that was programmed to record a fast and arrhythmic heart rate faster than the participant’s maximal heart rate from the exercise test, for ≥8 consecutive heartbeats. Athletes logged symptoms in a linked patient app. Here’s how they defined a “positive” arrhythmia event:

• Sustained ventricular tachycardia (VT): ≥30 seconds of ventricular rhythm at ≥100 bpm

• Non-sustained ventricular tachycardia (NSVT): ≥3 consecutive ventricular beats lasting <30 seconds

How often did arrhythmias show up?

During a follow-up period of just over 2 years (796 days), 55 ventricular arrhythmia events occurred in 25 athletes (23.5% of the participants): 52 NSVT (94.5% of all events) and 3 sustained VT (5.5% of all events)

Of the 55 events:

• 29 (52.7%) occurred during or within 1 hour of exercise

• 14 (25.5%) occurred 1–24 hours after exercise

• 12 (21.8%) occurred >24 hours after exercise

• 5 (9.1%) were nocturnal (occurred at night)

The incidence rate was 0.4 events per 1000 hours of exercise vs 0.01 per 1000 hours of non-exercise time. That’s a 28.6× higher event rate around exercise exposure.

Here’s where the study gets interesting. It identifies myocardial fibrosis as the clearest risk factor for ventricular arrhythmias in athletes. Among athletes who experienced an arrhythmia, 76% had evidence of fibrosis/scarring of the heart, compared to just 38% of athletes who did not experience an arrhythmia.

Furthermore, all 3 sustained VT cases (sustained VT being the longer-lasting, more serious form) occurred during exercise in athletes with fibrosis, and each was preceded by NSVT.

Premature ventricular contractions (or PVCs; early heartbeats occurring during maximal exercise testing) were another clear discriminator of who would go on to develop an arrhythmia during follow-up.

During exercise testing, 21/25 (84.0%) of athletes in the arrhythmia group experienced PVCs, compared with 35/74 (47.3%) in the no-arrhythmia group.

The influence of training characteristics

From the objective (TrainingPeaks/exercise device) data, we can see that most training characteristics were basically identical between groups who did and did not experience an arrhythmia.

• Weekly total exercise: 7.2 ± 3.1 hours (no arrhythmia) vs 7.2 ± 3.0 hours (arrhythmia)

• Weekly cycling distance: 1,17.2 ± 57.3 miles vs 1,10.0 ± 46.1 miles

• Weekly TSS: 338 (229–484) vs 366 (284–448)

• Annual cycling distance: 6,094 ± 2,979 miles vs 5,722 ± 2,400 miles

• Monthly session frequency: 14.5 (10.2–18.8) vs 16.4 (12.6–20.1)

They also looked for a classic endurance pattern: a big “training spike” in the weeks before an event. That didn’t show up in a clear way either.

Even in the month before each arrhythmic event (within each athlete), the big training load variables weren’t much different compared to the rest of the time when they didn’t experience an arrhythmia. Obviously, we can see small advantages here in the pro-arrhythmia months, but most didn’t reach statistical significance.

• Frequency: 16.8 (no arrhythmia) vs. 21.0 sessions (arrhythmia)/month

• Duration: 30.3 vs. 31.5 hours/month

• Distance: 480.8 vs. 483.3 miles/month

• TSS: 1,525.9 vs. 1,849.0 per month

The only meaningful-looking shifts were distributional (time in zones), not total load—athletes spent more time training in power zone 3 and less time in heart rate zone 2 in the month before experiencing an arrhythmia when compared to their “normal” month of training.

• Power zone 3 (tempo): 14.1% (no arrhythmia) vs. 15.1% of time

• HR zone 2 (endurance): 20.9% (no arrhythmia) vs. 15.8% of time

And when they grouped events by how soon after exercise they happened (<1 hour after exercise, 1—24 hours after, or >24 hours after), the preceding session metrics weren’t clearly different:

• Last-session duration: 0.9 hours (<1h) vs 1.2 hours (1–24h) vs 2.3 hours (>24h)

• Last-session distance: 16.2 miles vs 18.2 miles vs 34.2 miles

• Last-session TSS: 93.2 vs 94.2 vs 120.1

What’s this telling us? Doing more intensity, exercising for longer, or having a sharp spike in intensity doesn’t necessarily mean you’re more likely to experience an arrhythmia sooner after your exercise session (like 1 hour after) versus later (24 hours or more after). Again, I can see some trends here—larger training duration/distance/TSS did appear to make arrhythmias happen later after exercise as opposed to sooner. But I’m not quite sure there’s a clean interpretation there.

Other physiological differences worth noting

A few other differences that seem relevant:

• Athletes who experienced arrhythmias during the study had larger left ventricular volumes, on average (about 7.5% larger), compared to athletes without arrhythmias.

• Cycling functional threshold power (FTP) was similar between groups—250 Watts (arrhythmia) vs. 239 Watts (no arrhythmia).

  

  ---

Studies like this are why I’m still not persuaded by blanket claims that “excessive endurance exercise is heart-harmful.” That conclusion often gets built on proxy markers (e.g., CAC), small cohorts, or dramatic anecdotes, and then generalized to millions of people whose real endpoint risk is still seemingly low. This paper doesn’t let anyone off the hook (ventricular arrhythmias aren’t trivial, and fibrosis was detected in a meaningful number of the athletes), but it does push us toward a more discriminating view that risk factors and substrate may matter as much or more than the training itself.

In other words, heart risk may be less about demonizing endurance exercise and more about getting better at identifying who is susceptible, and why.

Because in this cohort, the guys with arrhythmias didn’t have higher weekly hours, distance, or training stress scores (as we might expect if more exercise is more harmful). Instead, myocardial fibrosis was associated with a much higher observed arrhythmia risk (fibrosis roughly tripled risk), and then acute exercise exposure clustered tightly around when events actually happened (nearly 29× higher event rate during and near exercise than away from it).

So the actionable thing for veteran endurance athletes (or those of us hoping to become them) is to not ignore symptoms during exercise, take abnormal heart sensations seriously, and recognize that the best risk reduction strategy may be identifying whether you have an underlying risk like fibrosis, rather than trying to perfectly tweak your intensity distribution.

When it comes to your heart, risk might be influenced more by who you are (and what your heart looks like) than your weekly mileage or how much time you’re spending in zone 2.

And if we really are to believe endurance exercise is harmful, in the words of legendary runner and writer Amby Burfoot: “Show me the bodies in the street.”2

Thanks for reading. See you next Friday.

~Brady~

Ketone-IQ is high-performance energy in a bottle. I use it for post-exercise recovery along with enhancing focus, mood, and cognition. Take 30% off your order.

Create is the first “modern creatine” brand. They sell a wide range of creatine monohydrate gummies—and yeah, their gummies actually contain creatine, unlike some other brands. They’re giving my audience 20% off their order.

ProBio Nutrition—the all-in-one supplement that I use every single day—is offering 20% off. My preference is the tangy orange flavor, but they also sell an unflavored “smoothie booster” that’s great in a shake, smoothie, or juice.

• Equip Foods makes some of the cleanest, best-tasting protein products around. I am absolutely obsessed with their Prime grass-fed protein bars (the peanut butter ones are to die for). Take up to 10% off site-wide and up to 35% off a subscription.

Greetings!

Welcome to the Physiology Friday newsletter.

Details about the sponsors of this newsletter and deals on products, including Ketone-IQ, Create creatine, Equip Foods, and ProBio Nutrition can be found at the end of the post.

I couldn’t wait to write about today’s study. It might be one of the most important and informative ones ever published on aging and physical performance.

The study followed the same people for nearly 50 years and found something illuminating: peak physical capacity arrives early (earlier than we think), starts to decline before age 40, and the slope of decline steepens with age.

And the most surprising (and actionable) part? Aging doesn’t just lower performance, it also widens the performance gap between people, suggesting the “inevitable” path of aging is modifiable.

This study will change how you think about your own “performance trajectory.”

The study tracked physical capacity across almost the entire “adult arc” of life, from age 16 to age 63, in 427 individuals (48% women) from a population-based Swedish cohort known as the Swedish longitudinal cohort for Physical Activity and Fitness, with five waves of measurements—at age 16, 27, 34, 51, and 63.1

This type of long-term follow-up in the same group of individuals is almost unheard of.

Gathering data on the same people decades apart, rather than relying on cross-sectional comparisons of different people, is crucial to understanding aging because it prevents misleading comparisons confounded by lifestyle factors. It tells us how physical aging occurs within a person rather than between different people.

To measure physical capacity, researchers focused on three domains:

• Aerobic capacity—assessed with a 9-minute run test at age 16 and a submaximal cycling test at ages 27, 34, 52, and 63, and then converted into VO₂ max.

• Upper-body muscular endurance—assessed using bench press repetitions to failure using 20 kg (for men) or 12 kg (for women).

• Muscular power—assessed using a standing vertical jump test.

What’s novel about the analysis is that they didn’t just focus on if and by how much physical capacity in each domain declines with age, but also when peak physical capacity occurs, the percent change in each domain per year after peak, and how much the spread in physical performance widens between people with age. The latter piece is perhaps one of the most underappreciated findings of the entire paper.

Physical aging is inevitable

Here’s the headline finding—peak physical capacity occurs before age 36, and after age 40, the decline is shared across domains, accelerates as people get older, and is unavoidable… but the rate is modifiable!

Regardless of the domain being measured, most people’s physical capacity peaks around or before age 36, and the annual loss in capacity is about 1% per year shortly after the peak and accelerates to more than 2% per year later on. The average loss in physical capacity from peak to age 63 is about 37%, spanning roughly 30% to 48% depending on the domain (more on that below).

This is a very different story from what we’re currently told about aging—that things start to go downhill around age 50 or 60. These data would suggest that the slide starts earlier than most people realize and may be a “built-in” biological phenomenon.

Alright, let’s now get to each physical performance domain, because these data are even more interesting than the general trend.

All physical domains decline with age (but not equally)

Absolute aerobic capacity (VO₂ max in Liters per minute) peaks at age 35–36, after which it declines by 0.5% per year up to age 40 and then up to 2% per year by age 63. And across the entire population, absolute aerobic capacity is 30–33% lower at age 63 than at peak capacity (age ~35).

Relative aerobic capacity (VO₂ max in mL/kg/min) follows the same trend and is perhaps the more important measure of aerobic fitness because it blends the “engine size” with changes in body mass (which also changes with age). The peak age differed between men and women: men peaked at 26 (42.2 mL/kg/min) while women peaked at 31 (39.7 mL/kg/min). But the rate of decline was similar—from age 40 onward, relative VO₂ max dropped by 1.1% per year. By age 63, the rate of decline was 2.2% per year. And the cumulative decline in aerobic capacity from peak to age 63? It was 37% in women and 40% in men.

These numbers are pretty drastic, especially the cumulative decline. They indicate that by age 63 (early old age), most people have lost nearly half of their peak aerobic capacity.

Looking at muscular endurance, the peak age was similar to that for aerobic capacity—age 34 in women and age 36 in men—and the rate of decline accelerated from around 1% in the early years after peak to just over 2% per year by age 63. The cumulative decline in muscular endurance from peak to age 63 was 32% for women and 35% for men.

Last is muscular power (vertical jump). This domain had a vastly different trajectory than aerobic capacity and muscular endurance. It peaked early, at age 19 in women and age 27 in men, after which it declined by 1–2% per year from age 30 onward. By age 63, men and women had lost 41% and 48% of their peak muscular power, respectively.

Aging increases the performance inequality

One of the most novel aspects of this study was that it quantified how much between-person variance increases from adolescence to early old age. In other words, how does the gap in performance (the “spread”) change between people as they get older?

Depending on the capacity, variance increased roughly 3-fold to 25-fold!

To use specific examples, the variance in relative aerobic capacity was 25 times higher at age 63 than at age 16, while for muscular endurance it was 3 times higher, and for muscular power it was 5 times higher.

What does this mean practically? At age 16 (and perhaps even up to peak capacity), the performance gaps between any two people are generally low, but by age 63, two people with the same chronological age may have a wildly different “functional age,” and the gap between them is much bigger than it was at age 16.

I think this is much more than a statistic. It’s telling us that while aging may be a universal process, performance is an individualized outcome shaped by one’s environment, lifestyle, and physiology.

That brings us to the final component of this paper—the influence of physical activity.

People who reported being physically active at age 16 (though admittedly through a crude “yes/no” answer on a questionnaire) showed a higher capacity later on in life at every age. That much might seem obvious. But what’s interesting is that switching from being “inactive” to “active” at any age was associated with meaningful gains across domains—a 6–7% bump in aerobic capacity, an 11% bump in muscular endurance, and a 4% bump in power output.

However, activity didn’t appear to shift the age of the peak in physical performance, just the slope of the decline after the peak. This would suggest that when we reach our peak capacity may be constrained… perhaps even encoded in our biology. But how fast we decline after we reach our peak (and the ceiling we do reach) is meaningfully modifiable.

They reinforce that conclusion by adding some data comparisons from the literature on master athletes. At age 63, the general population maintained only about 65% of their peak aerobic capacity and muscular endurance, whereas athletes of the same age maintain about 80%. And for muscular power, men and women from the general population have kept only about 50–60% of their peak capacity at age 63, but it takes athletes until age 75 to hit a comparable level of decline.

The narrative here is clear: aging is inevitable, but how fast you age is negotiable.

Why did I start this newsletter by emphasizing how excited I was to share this paper? Hopefully, my discussion above made that clear, but if not, it’s because I think it radically shifts our perception about the trajectory of aging and how modifiable it is (in addition to just being a really cool and novel design…just a unicorn of a study).

If the rate of aging is innate—as the data here would suggest—but the starting point in our position on that hill (how high we peak) is up to us, it raises an important question: Are we really fighting aging when we exercise, or are we simply just fulfilling the maximum potential of a decaying system?

Maybe exercise isn’t magic or medicine. It’s proper maintenance for the machine, without which we experience premature decline.

We often accept the average as being inevitable, especially when it comes to health. We see the decline as the tragedy.

Maybe the real tragedy is that most people never get close to seeing what their 60- or 70-year-old body is capable of.

We are normalizing disuse and calling it aging. And I think it’s time to change that.

Thanks for reading. See you next Friday.

~Brady~

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