Update on Brad Stanfield's Rapamycin Clinical Study in NZ

#222

No, it was because of food poisoning.

#223

I hear you, but apparently, it either didn’t help him much (but he thought it did), or it wasn’t enough help. Either way he’s dead and he was taking RAPA. Not good in my books regardless of what anyone thinks. I definitely need to see cases of people being in their late 90’s and hopefully 100’s that seem relatively healthy and have been take RAPA for a while to be convinced. Until such time count me as being very sceptical, while I am still taking it though, which is to say I’m not against it yet. But instead of being say 100% pro, I’m more like 60-65 pro and 35-40 against. Everything else I take I’m 100% pro, not even a 1% doubt that I’m benefiting (greatly) by taking them.

Again, I think Rapa is a perfect case of reward vs risk. For some people there seems to be no risks (or very low) for others there are risks of lipid and glucose moving in wrong direction plus infections and other sides. I always love it when I take something and don’t feel any negative side effects while reaping the benefits. and, for people with no side effects from RAPA I do think they are benefiting from it overall. But if you happened to have to take other meds to counter the side effects of RAPA as an example (or any other med for that matter) then to me it beats the purpose. Why not stick with the substances/meds that you know are not negatively affecting you, yet they are benefiting you greatly with better markers, while still showing pretty convincingly longevity benefits, i.e. SGLT2i’s, Acarbose and many others.

Again, I don’t call it a gamble at all if I take something and have ZERO side effects while the benefits show right away in the labs. As I said earlier been taking Empa, EZE, Telmi, Pita, ACA, and meftormin all of which have shown to increase lifespan (some more, some less) and I have absolutely no negative side effects. I love them! As far as RAPA, well can’t say I love it. One thing is for sure that If I get one more cold/flu case with symptoms near or as bad as last one I had couple weeks ago (it started two days after taking my Rapa dose) you can rest assured that all my 1000 pills of RAPA will be thrown in the garbage, but being that I’m a bit of vengeful type of guy, I might actually burn them first, and to NEVER look back at it again LOL. No, thank you I’m not going to continue taking something that continues to make me sick, and If I were to continue then I’d agree with you, that is pure gambling. As far as the other 5 or six I listed earlier and I love don’t see much gambling there. Even my anion GAP (whatever that is) that has persistently been higher for last five years dropped to “optimal” out of nowhere and i didn’t even expect it at all on my last test. Out of convenience, (and maybe a good guess) I dedicate that improvement to SGLT2i/EMPA even though I had started couple other substances at same time and after my last test (i.e. Telmi and Sele) but it definitely wasn’t Rapa because it kept showing as too high even long after I had started Rapa.

Just my unvarnished opinion but in all honesty if I were to have no side effects from it, or if my sides somehow disappear going forward, I will definitely continue using it.

#224

The mice in the ITP study also appeared to have metabolic disfunction yet they lived longer, part of the answer is in the following study

And don’t know how many people got rapamycin in the Stanfield study but by now there are many of us here, taking it for many years now and I don’t think anyone of us got any serious side effects definitely attributed to rapamycin

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#225

The ITP paper does not explicitly discuss or provide data to demonstrate whether rapamycin increases or improves specific metabolic dysfunctions. Could you please share your data source?

Moreover, even if both humans and mice(ITP) exhibit metabolic dysfunction, does that prove that this can be used to extend human lifespan?

#226

I was mistaken, it wasn’t from the actual ITP studies but from other studies with rats/mice on a similar feeding regime, eg

but I don’t see how that would have been different for the ITP mice

#227

Never mind. I had a feeling I might get a lot of criticism, so I deleted my original comment. I’ve watched all his interview videos, and there are quite a lot of outrageous things.

#228

I think you are talking about “clinical trials” that use certain biological or epigenetic clocks, which we know are not ready for prime time and that do not measure all aspects of aging.

It’s hard to ascertain people’s motivations, but he seemed to genuinely want to help people. Cole, I think you’re jumping to conclusions as we have no evidence for his true motivations. And, you seem not to know that he died a few years ago: Dr. Alan Green, a great pioneer in Rapamycin usage, passed away yesterday in Little Neck, NY

Dr. Green worked with Matt Keaberlein, and many people here on the forum, to study and learn from his patients, which resulted in this paper: Evaluation of Off-label Rapamycin use to Promote Healthspan in 333 Adults (New Paper)

and: No Covid Hospitalizations or Deaths in Dr. Alan Green's 700+ Rapamycin Users

#229

I hear you but need a small correction. I think you might need to use past tense when you refer to him. I doubt he “IS” any longer because he’s been dead for over a year now LOL, other than that I do get your point :joy:

#230

An analysis of Cole’s analysis…

Critical Analysis of the Trial Evaluation

The provided analysis of the 13-week human trial is heavily skewed toward a binary “pass/fail” interpretation of gerotherapeutics. While the raw data accurately reflects the outcomes of that specific trial, the conclusions drawn from it contain significant mechanistic blind spots and biological misconceptions.

Here are the primary flaws in the analysis, cross-referenced with the 2026 Hibbert et al. Science Advances data and broader pharmacological literature.

1. The Hypertrophy Fallacy: Conflating “Power” with “Adaptation”

The analysis concludes that rapamycin “blunts muscular strength adaptations” and provides “zero measurable physiological or functional benefits” because of lower repetitions in the 30-Second Chair-Stand Test. This is a profound misinterpretation of muscle biology.

  • The Flaw: The chair-stand test primarily measures explosive concentric power, which is directly dictated by the cross-sectional area (thickness) of muscle fibers. The Hibbert et al. (2026) [cite_start]study definitively proves that this specific type of growth—radial growth—is mediated entirely by the rapamycin-sensitive mTORC1 pathway[cite: 13, 167]. By administering a 6 mg weekly dose, the trial successfully inhibited mTORC1, thereby predictably blunting radial hypertrophy and the resulting concentric power generation.
  • The Ignored Benefit: The analysis completely ignores the existence of longitudinal growth. [cite_start]Hibbert et al. demonstrated that mechanical loading induces the in-series addition of new sarcomeres (lengthening the muscle fiber) via a completely rapamycin-insensitive mechanism[cite: 13, 172, 703]. [cite_start]Claiming there are “zero physiological benefits” ignores that these subjects were likely still undergoing profound structural remodeling, increasing fascicle length and altering contraction velocity, even while their radial “power” adaptations were chemically locked[cite: 706, 770].

2. Mischaracterization of the “Starvation Phenotype” as Toxicity

The analysis points to elevated HbA1c (+1.74 mmol/mol) and LDL cholesterol (+0.32 mmol/L) as “objective proof” of “measurable degradation” and metabolic dysfunction.

  • The Flaw: In the context of mTOR inhibition, these lipid and glucose shifts are not necessarily indicative of pathological toxicity; they are well-documented features of a state often called “pseudo-diabetes” or the “starvation phenotype.”
  • When mTORC1 is inhibited, the body mimics a state of nutrient scarcity. It suppresses lipid storage (resulting in a transient rise in circulating LDL as lipids remain in the blood) and initiates peripheral insulin resistance to spare circulating glucose for the brain (slightly elevating HbA1c). Equating an adaptive survival phenotype to pathological metabolic disease is a common error in translating standard clinical biomarkers to longevity interventions.

3. The Epigenetic Time-Horizon Fallacy

The author asserts there is “zero quantitative evidence” of anti-aging benefits because epigenetic clocks (GrimAge, etc.) showed negligible differences over the 13 weeks.

  • The Flaw: Epigenetic clocks track the long-term, cumulative methylation changes of cellular aging. Expecting a 13-week (91-day) protocol to yield statistically significant, systemic reversals in human DNA methylation is biologically naive. The absence of epigenetic age reversal in a single financial quarter is a limitation of the study’s duration, not definitive proof of the drug’s inefficacy as a geroprotector.

4. Statistical Misdirection Regarding Inflammation

The analysis states that mean C-Reactive Protein (CRP) increased by 4.26 mg/L, framing this as a failure of rapamycin’s anti-inflammatory properties, while simultaneously admitting the data was skewed by two massive outliers (17 mg/L and 50 mg/L).

  • The Flaw: A CRP of 50 mg/L indicates an acute phase response—typically a severe bacterial infection (which perfectly aligns with the single reported Serious Adverse Event of community-acquired pneumonia). Allowing acute infection outliers to dictate the mathematical mean, and then using that skewed mean to declare the drug lacks basal systemic anti-inflammatory properties, represents poor data interpretation.

5. Protocol Failure vs. Mechanism Failure

The overarching conclusion strips away “theoretical optimism” to declare a “definitive negative result” for the drug.

  • The Flaw: The trial does not prove that rapamycin is a failure; it proves that this specific protocol (combining an exercise intervention with a high-trough 6 mg weekly dose) creates a biological conflict of interest. You cannot maximally stimulate radial muscle hypertrophy (which requires mTORC1) while simultaneously dosing a compound designed to block it. The blunted functional gains are a failure of timing and pharmacokinetics, not a failure of the molecule’s longevity potential.
#231

It’s hard to even know what to make of this AI-on-AI analysis. The study only looked at short-term data, and every conclusion is strictly qualified by that 13-week window. If I ran your critique through an AI again, it would just hallucinate even more errors.

#232

You stated this in your analysis. Are you now saying you don’t agree with your post? Why post it if you don’t agree with it?

Your posting / analysis seems to largely be a “straw man” analysis. It was a short-term muscle study, it tell us nothing about the long-known benefits of longevity that rapamycin has demonstrated in dozens and dozens of studies.

#233

The original commentary explicitly limited its conclusions to a 13-week or short-term timeframe, as that is what the raw data reflects.

I simply used Gemini Pro to analyze the data from this specific paper, using prompts strictly designed for objective analysis. Therefore, any conclusions are limited to the scope of this study alone. I am confident that the AI followed my instructions and did not extrapolate findings beyond the actual timeframe of the trial.

#234

I guess the key thing we learned here is that 13 weeks is not an appropriate period to test a longevity drug.

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#235

Exactly. The analysis was strictly limited to the data in that paper, and I certainly didn’t bias the prompts to favor any specific outcome. Gemini Pro’s use of ‘anti-aging’ was merely a description of the epigenetic clock, and ‘anti-inflammatory’ referred specifically to the C-Reactive Protein levels. If those terms are problematic, I’m happy to remove them and leave only the raw data. Since those descriptions seem to have touched a nerve, I’ll ensure the AI strips away any descriptive labels for the metrics next time.

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#236

Gemini Pro:
Approximately 0.81% of the total U.S. population is male and 85 or older. This accounts for roughly 2.8 million male individuals.

Well, I am finally in a group of less than 1%; though rare, you see us everywhere. Apparently a high dose of rapamycin for five years isn’t killing me.

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#237

I take follistatin 344 thirty minutes before each workout. Hopefully that overcomes the adverse effect of Rapamycin.

JPK

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#238

Yeah, I’m familiar with this paper. Alan Green mentioned in an interview that Matt Kaeberlein personally flew from Washington to New York to get an email list from him of 900 patients taking rapamycin for anti-aging. Professor Kaeberlein sent out surveys to all of them and got over 300 replies from rapamycin users, which formed the basis of this study. I bet a lot of people on this forum actually filled out that questionnaire. To be honest, I haven’t read the paper that closely. For me, the most important takeaway is that 90% of people using rapamycin for anti-aging are on a 6mg dose.

Haha, actually I’m more curious about how many dollars Matt Kaeberlein paid those patients as a thank you for filling out the questionnaire. The response rate was quite high; if they didn’t receive any compensation, then those patients were truly selfless.

#239

None of us were paid for the study participation. We all want to move the science forward.

#240

Given that this paper is so important and represents the hard work of biohackers, posting comments indeed requires very careful and repeated consideration. However, I noticed that no one seems to have uploaded the PDF version of this paper. Although it claims to be free, downloading it without an institutional account incurs a fee. Therefore, I’ll take the opportunity to upload the PDF version without offering any interpretation.

After some thought, perhaps no one uploaded it because of commercial copyright concerns. Making the PDF publicly available might not be appropriate. After further consideration, I decided to delete it. It seems that only sharing it via private messages would be legal.

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#241

I. Research Methods and Basic Information

  • Data Collection Method: Retrospective online survey, with all data self-reported by participants.
  • Total Sample Size: A total of 505 individuals, including 333 rapamycin users and 172 non-users.

II. Demographic Characteristics and Lifestyle Indicators

  • Age Distribution:
    • Male users: average 61.1 ± 11.6 years; Male non-users: average 51.8 ± 14.6 years.
    • Female users: average 60.7 ± 10.5 years; Female non-users: average 56.4 ± 12.2 years.
  • Lifestyle Data:
    • Regular exercise: User group 89.6% (men), 84.9% (women); Non-user group 84.4% (men), 87.3% (women).
    • Healthy diet: User group 97.3% (men), 93.2% (women); Non-user group 97.2% (men), 98.4% (women).
    • Fasting/Time-restricted feeding (TRF): User group 70.4% (men), 63.0% (women); Non-user group 61.5% (men), 71.4% (women).
    • Tobacco use: User group 1.2% (men), 0% (women); Non-user group 1.8% (men), 4.8% (women).

III. Rapamycin Usage Data

  • Purpose of Use: 95% (313 individuals) of users reported the reason as “healthy longevity/anti-aging”.
  • Prescription Status: 77.7% of male users and 82.2% of female users took it under the supervision of a physician.
  • Dosage and Frequency:
    • The reported equivalent weekly dose ranged from 1 mg to 20 mg.
    • The most common dosing interval was “once weekly” (88.1% of men, 91.8% of women).
    • The most common equivalent weekly dose was 6 mg.
  • Duration of Use: The median length of time on rapamycin among all users was 218 days (ranging from 1 to 3890 days).

IV.

Health Conditions and Signs in the Past 3 Months (Users n=245, Non-users n=172)

  • Statistically Significantly Higher Symptoms (p < 0.05):
    • Mouth ulceration: Users 14.7% vs. Non-users 4.7%.
  • Statistically Significantly Lower Symptoms (p < 0.05):
    • Abdominal cramps: Users 2.0% vs. Non-users 14.0%.
    • Abdominal pain: Users 8.6% vs. Non-users 20.9%.
    • Muscle tightness: Users 29.8% vs. Non-users 45.3%.
    • Depression: Users 4.1% vs. Non-users 15.1%.
    • Anxiety: Users 7.3% vs. Non-users 18.0%.
    • Eye pain: Users 3.7% vs. Non-users 11.6%.
  • Specific Infection Data Not Reaching Statistical Significance:
    • Respiratory tract infection: Users 14.7% vs. Non-users 8.1%.
    • Skin infection: Users 6.1% vs. Non-users 2.3%.
    • Urinary tract infection: Users 2.9% vs. Non-users 1.2%.

V. Self-Reported COVID-19 Infection Data

  • Overall Infection Rate: The user group reported an infection rate of 28.5% (95 cases), while the non-user group reported 31.3% (54 cases).
  • Disease Severity (Classified by Usage Group):
    • Continuous user group (n=37): Took rapamycin before, during, and after infection. Mild 88.5%, Moderate 13.5%, Severe 0%, Long-COVID symptoms 0%.
    • Non-user group (n=54): Mild 50.0%, Moderate 46.3%, Severe 3.7%, Long-COVID symptoms 5.6% (3 cases).
  • Statistical Results: Continuous rapamycin users were significantly less likely to have experienced a moderate or severe infection compared to non-users (p < 0.005).

VI. Subjective Self-Evaluation Questionnaire Results

  • In the subjective agreement survey regarding rapamycin user experiences, the top three indicators were:
    • “My health has improved since taking rapamycin”: 44.7% Agree, 5.7% Disagree.
    • “I feel younger”: 37.5% Agree, 5.7% Disagree.
    • “I have more energy”: 38.7% Agree, 6.3% Disagree.
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