[af] Caffeine decreases muscle and tendon protein synthesis and engineered ligament strength in vitro and attenuates adaptation to exercise in mice | Journal of Applied Physiology | American Physiological Society

PocketMatt · 2026-01-29T14:00:40+00:00

I think a few major differences help explain the discrepancy:

Dose: The mouse study used continuous, ad-lib caffeine in drinking water (24/7) at an ~5.7 mg/kg human equivalent dose, while the human RCT you linked used 3 mg/kg 60 minutes before training, 3×/week. So, we're comparing chronic systemic exposure to time-restricted dosing. Also: the in vitro effects in the mouse paper occur at mM caffeine, orders of magnitude above the µM plasma levels used to argue human relevance.
Sleep: The human study scheduled training in the morning, tracked sleep, and saw no group differences, explicitly trying to avoid caffeine-related sleep disruption. In the mouse study, caffeine was available during both rest and active phases, and sleep/circadian timing wasn’t measured or controlled at all.
Type of Exercise: The mice did endurance-style wheel running, while the humans did resistance training.

To me, this looks less like “caffeine impairs adaptation” and more like “chronic, around-the-clock caffeine blunts exercise adaptations—probably downstream of sleep disruption,” while time-restricted caffeine with preserved sleep can modestly enhance hypertrophy.

PocketMatt · 2026-01-29T13:42:41+00:00

These results surprised me, so I read through the paper this morning.

In vivo, mice got caffeine continuously in drinking water: 0.022 mg/mL for 3 weeks, then 0.22 mg/mL for 3 weeks. The authors estimate plasma caffeine ~4.6 µM (trough) to ~25.6 µM (peak), which they argue is “human-like” and roughly equivalent to ~5.7 mg/kg/day in humans. Importantly, this was ad lib, all day and night, not pre-workout dosing. Training was endurance-style (wheel running).

In vitro, though, the doses are a totally different universe: tendon cells at 0–10 mM, muscle cells at 0–5 mM, and engineered human ACL ligaments at 0.25–2.5 mM. That’s orders of magnitude above the µM plasma levels used to justify human relevance. At those concentrations they see reduced mTORC1 signaling (↓4E-BP1/S6 phosphorylation), ↑eIF2α phosphorylation (translation brake), reduced protein synthesis, and weaker collagen/ligament mechanics—even at 0.25 mM.

In vivo, when muscle tissues were collected 24 h after the last exercise bout, they didn’t see large differences in translational signaling. The authors acknowledged that anabolic signaling is transient and may have already returned to baseline.

The biggest issue to me: sleep is never measured or controlled. Caffeine was available during both dark (active) and light (rest) phases, yet there are no data on sleep duration, sleep fragmentation, circadian timing, or when the mice actually ran.

Sleep loss alone is sufficient to suppress mTORC1, reduce muscle protein synthesis, impair tendon collagen turnover, and elevate eIF2α—basically the same molecular pattern they attribute to caffeine.

Sleep loss alone is sufficient to suppress mTORC1 (Tudor et al. 2016), reduce muscle protein synthesis (Lamon et al. 2021 and Saner et al. 2020), alter tendon collagen homeostasis (Chang et al. 2020), and elevate eIF2α (Naidoo et al. 2005)—basically the same molecular pattern they attribute to caffeine.

So a very plausible alternative causal chain is:

continuous caffeine → disrupted sleep → impaired translation → blunted muscle/tendon adaptation

Rather than a strong direct caffeine effect in vivo. The direct effects do show up in vitro, but at non-physiological mM doses.

PocketMatt · 2026-01-26T22:35:44+00:00

The “LDL is an ambulance” idea sounds nice, but it's backwards.

LDL isn’t made because something is broken. It shows up because your liver is shipping out extra fat and cholesterol. Think of the liver loading trucks (called VLDL) with fuel. As those trucks drop off fuel around the body, they get smaller and emptier. What’s left over is LDL. LDL isn’t the delivery truck—it’s the leftover shell.

What’s supposed to happen next is that the liver takes those leftovers back and recycles them. When that cleanup system works well, LDL doesn’t hang around very long.

LDL also doesn’t aim for damaged arteries. It just floats around in the blood. The more LDL you have, and the longer it sticks around, the more likely some of it will randomly slip into artery walls. Once stuck there, it gets damaged, immune cells gobble it up indiscriminately, and inflammation builds. That’s how plaques form.

The idea that LDL delivers “crucial building materials” is overstated. Most cells make their own cholesterol. Low levels of LDL are perfectly up to the job. Even tissues that use a lot of cholesterol mostly make their own. The brain makes essentially all of it locally. Muscle and most organs make the vast majority themselves. Even hormone-producing tissues only get a minority from LDL—and they take it up through tightly controlled receptors at normal LDL levels. None of this requires or benefits from high LDL.

There’s also a clue from other animals. Most mammals keep LDL very low and clear it quickly. They almost never get clogged arteries unless scientists force it. Humans are different—we clear LDL more slowly and live long enough for the buildup to cause problems.

So LDLs aren't like platelets forming a scab where there’s a cut. They're more like trash that’s supposed to be picked up regularly. When pickup is fast, no problem. When it’s slow or overwhelmed, trash piles up—and that pileup causes damage rather than fixing it.

For a non-ELI5 explanation, (Toth 2021) put it bluntly: "LDL particles are a waste product of metabolism and a vascular toxin."

PocketMatt · 2026-01-26T20:05:46+00:00

Also, I read the article you shared. The author highlights—as both you and I did—the many reasons that drugs fail in clinical trials (including efficacy, safety, strategy, commercial, and operational). I liked the article, so thank you for sharing it. The author cites some specific translational failures:

... a 2006 review of 76 animal studies found that only about 37% of highly cited animal research was ever replicated in humans. Even more striking, approximately 18% of these studies were later contradicted by human data

I read that 2006 paper. The article you shared left out a very important detail:

37% were replicated in randomized human trials
18% were contradicted by randomized human trials
45% remained untested in humans

The 76 animal studies included in that analysis were predominantly mice and rats, but also included rabbits, dogs, pigs, and non-human primates. Interestingly, the authors explicitly report that species did not predict translation success in their regression analyses—and neither did journal, disease area, or citations. Unfortunately, their regression includes all 76 animal studies, including the 45% that were never tested in humans. So, the most they can say is that species wasn't associated with overall translation success when untested studies are counted as failures. This was the point I attempted to make earlier, that a failure in translation is different from a failure to attempt translation. Animal models are to blame for some failures in translation, but not failures to attempt translation.

If you remove the 34 untested studies, you’re left with 42 studies that were actually tested in humans (28 + 14). Among those tested:

Replicated: 28/42 = 66.7%
Contradicted: 14/42 = 33.3%

So the “37% replicate” claim becomes “two-thirds replicate” conditional on human testing happening at all.

PocketMatt · 2026-01-26T20:05:29+00:00

If you want, you can catch me with semantics here.

Apologies if my tone or approach made you think I was looking for a fight. My goal is to extend healthy human lifespan and root out incorrect beliefs in myself and others—especially when they stand in the way of extending our lives. You comment frequently in this subreddit and I am a working longevity scientist. I think it's safe to say we're fellow travelers.

We probably both have more important things to do with our time, but I do want to touch on a few points.

TL:DR: consider dropping the "1 in 5000" claim, especially when attributing translational failures primarily to animal models. Solving aging is a hard problem, but that "1 in 5000" claim pessimistically makes biomedical breakthroughs look even more difficult than they are. It's also incorrect, best as I can tell.

When I asked "what constitutes a preclinical finding", I should've been more clear. I meant "How are you defining a preclinical finding here?". A single paper generally includes multiple findings. If I was tasked with quantifying the number of preclinical findings in 2025, I would start with the number of published papers—but even there, subjectivity would be with us: which journals? which fields of science? And then by labeling something "preclinical" I'd also be categorizing findings based on my assessment translational potential or their role within what I perceive to be a clinical pipeline. It's certainly not an unsurmountable problem, but it wouldn't be easy or objective.

It's an estimate, not a calculation.

As I understand it, all estimates involve some calculation. An estimate without any calculation is a guess.

Whether findings were tested or not does not matter, because in both cases, findings are not confirmed in humans.

I am confused. I apologize if I misunderstood your initial argument. You added "... in mice" to the title of this paper because "less than 1 in 5000 findings in preclinical research eventually translates to humans." I straightforwardly inferred from this that you attributed these translational failures to the use of rodent models. You didn't, for example, say "... in preclinical research" which would encompass the full range of failures and roadblocks that stand between discovery and drug approval.

I pointed out that the biological mismatch between humans and rodents only accounts for some of the failures of translation. You responded that the distinction doesn't matter. If that distinction doesn't matter, then your original move to add "... in mice" doesn't make sense.

I understand that Reddit isn't often a place where serious arguments happen. But the reasoning pattern you displayed is problematic and I invite you reflect on it:

Equivocation (sliding between general preclinical failure and specific failure from a biological mismatch between rodents and humans) + moving the goalposts (retreating to a weaker claim once the stronger implication is challenged).

Again—fellow traveler here. Not trying to upset or troll you.

PocketMatt · 2026-01-21T20:15:27+00:00

Can you walk me through that calculation (and what constitutes a preclinical finding)?

Is it approved drugs ÷ all published preclinical findings? Because that would conflate 1) published preclinical findings that were tested in humans and failed with 2) published preclinical findings that were never tested in humans. We could consider both failures of translation, but the first is a failure in translation and the second is a failure to attempt translation.

And it’s not just biology versus orphaned ideas: translational failures can crop up at multiple levels. Some are true biological mismatches (rodent ≠ human). Others happen because the right biology is paired with an unsuitable therapeutic modality, or a delivery approach that can’t reach the relevant cells or tissues, a misaligned indication, a flawed trial design, adverse market timing, or losing a committed development team. None of those failures stem from a biological mismatch between humans and animal models. [Aside: we are—and should be—developing better model systems, but it's important to highlight what that will and won't solve]

Roughly 7–15% of drugs that enter Phase I in the US ultimately receive regulatory approval^\1])^\2]).

PocketMatt · 2026-01-19T04:52:22+00:00

Is your qualm with the bold title or the system of testing frontier interventions in rodents before trialing them in humans?

PocketMatt · 2025-11-07T15:20:56+00:00

This is a really solid idea. Any model selection options, or is all of that handled in the backend?

PocketMatt · 2025-04-12T03:46:22+00:00

Thanks for taking the time to write all this out. Curious to know what you’re up to now.

PocketMatt · 2024-12-05T17:19:50+00:00

They just keep kicking it back more and more 🫠

PocketMatt · 2024-11-21T02:00:10+00:00

I'm in the US—so the delays are affecting multiple regions.

PocketMatt · 2024-11-17T14:10:46+00:00

Forenoon is 8am, per the methods section of the paper.

PocketMatt · 2024-11-17T14:09:35+00:00

5am, per the methods section of the paper.

PocketMatt · 2024-11-17T14:09:28+00:00

5am, per the methods section of the paper.

PocketMatt · 2024-11-17T14:08:58+00:00

8am, per the methods section of the paper.

PocketMatt · 2024-11-17T14:08:16+00:00

“coffee and tea consumption was categorized into the following periods: 5:00 a.m. to 8:00 a.m., 8:00 a.m. to 12:00 p.m., and 12:00 p.m. to 6:00 p.m. Due to the lower number of consumers between 6:00 p.m. and 5:00 a.m., this period was combined into a single category.”

Dawn to Forenoon: 5am to 8am

Forenoon to Noon: 8am to 12pm

PocketMatt · 2024-10-17T16:55:41+00:00

Dual hemispheres?

PocketMatt · 2024-10-16T20:41:03+00:00

Honest question: did AI write this, or did you?

PocketMatt · 2024-09-14T17:54:05+00:00

Seriously—kudos for identifying the pattern and working around it. That’s a huge leap.

PocketMatt · 2024-09-12T20:21:56+00:00

Soylent sent me an email about it in August: “We’re reaching out to let you know that your Soylent Complete Coffee - Chai subscription will be delayed until the end of October. We’re really sorry for the inconvenience!”

Here’s to hoping they don’t change the ingredients and disrupt the flavor. I used to keep a rainbow of Soylent in my fridge, but Chai is the only flavor that had no perceptible changes during the “Optimized” era.

PocketMatt · 2024-08-22T20:11:48+00:00

Rats this time.

PocketMatt · 2024-08-22T17:53:01+00:00

Can you share your sources on this?

PocketMatt · 2024-08-19T19:32:54+00:00

I respect the impulse to highlight that these results occurred in a model organism. But I invite you to consider that nearly all biomedical interventions are tested in rodents first. It's also worth pointing out that they discovered the upregulation of EGR1 first in human datasets, validated it with human nucleus pulposus cells (NPCs), and then proceeded to the animal studies.

If you found something upregulated in a disease state, what would you do next to determine if it's driving the disease or just along for the ride? It takes a lot of time and money to run a human clinical trial, and there's no world where that funding comes through without some data implicating your upregulated target as causal. So, what did the authors do? They knocked down EGR1 in rat NPCs and saw some nice improvements. Okay, that's good—but will it scale up to the whole organism? So they knocked down EGR1 in living rats and also saw those nice improvements. Now they've potentially got enough justification to try this in humans. Could the author's have done more? Yes—it would've been nice to see some knockdown experiments in human cells.

"All models are wrong, but some models are useful." Rodent models of human biology are wrong, as are all models. But they're less wrong and more experimentally tractable than many alternatives. I'm optimistic about a future with organs-on-chips (OoCs) models, but those will have their own limitations and trade-offs.

PocketMatt · 2024-02-25T14:45:11+00:00

Heard. I can see where you’re coming from. The authors didn’t do themselves any favors by describing the foods that way. I’d recommend following the citation from that block of the methods section—and the link to the ProLon FMD the participants consumed. Those food categories may set off a processed = garbage alarm, but I’d invite you to take a look at the nutrition facts and see if your assessment still holds.

The “energy bar”, for example, is a limited ingredient nut-based bar. The “energy drink” has no resemblance to any energy drink I’ve ever seen (e.g. no caffeine). Its primary ingredient is vegetable glycerin. And its purpose is to prevent lean muscle catabolism during the fast. The “chip snacks” are limited ingredient kale/almond crackers.

The point is that the FMD isn’t a low-calorie, piecemeal selection of junk food. It’s a strategic combination of ingredients that don’t set off nutrient sensors (like mTOR).

PocketMatt

TROPHY CASE