I’m talking about the adjustments in the study.
Still the same unless every participant was eating a caloric deficit. Probably need metabolic chambers too.
Basically next to worthless, but it’s hard to do better.
The concept of limiting amino acid(s) is pretty relevant to our discussion. What does ‘limiting’ mean exactly? I find the answer from Deepseek pretty educational
Q:
So, is the concept of limiting relative to a limitation in the synthesis of new protein in the body?
A:
Yes, absolutely. The concept of a limiting amino acid (LAA) is fundamentally tied to a limitation in the synthesis of new proteins within the body. Here’s a precise breakdown of the mechanism:
1. The Core Limitation: Disrupted Protein Synthesis
- Primary bottleneck: When one essential amino acid (EAA) is deficient relative to the others in a meal or dietary pattern, it directly restricts the body’s ability to assemble new proteins.
-
Ribosomal “Assembly Line” Analogy:
- Ribosomes build proteins by linking amino acids in genetically determined sequences.
- If any single EAA is missing or insufficient, the ribosome cannot complete the polypeptide chain.
- The limiting amino acid acts like a missing part that halts the entire assembly process.
2. What Happens Biochemically
- Stalled translation: Protein synthesis (translation) pauses or stops at codons requiring the limiting amino acid.
- Amino acid pool imbalance: Non-limiting EAAs accumulate temporarily but cannot be stored for later protein synthesis.
-
Catabolism (Breakdown): Excess non-limiting amino acids are:
- Deaminated (nitrogen removed).
- Converted to glucose (gluconeogenesis) or ketones.
- Oxidized for energy or stored as fat.
- Nitrogen excreted as urea.
- Result: The protein’s biological value plummets. Energy is wasted metabolizing excess aminos.
3. Limiting Relative to What?
The limitation is defined by two interdependent standards:
-
A. The Body’s Immediate Demand for Protein Synthesis
Example: To build muscle after exercise, all 9 EAAs must be present in sufficient quantities simultaneously. If leucine is low relative to need, synthesis stalls. -
B. The Idealized Reference Pattern (e.g., WHO/FAO Amino Acid Requirements)
This pattern reflects the precise ratios of EAAs needed to support human growth, repair, and function. A food’s “score” (PDCAAS/DIAAS) quantifies how its profile matches this ideal.
4. Consequences Beyond Synthesis
While disrupted protein synthesis is the primary bottleneck, LAAs also affect:
- Repair: Tissue maintenance slows.
- Enzyme/Hormone Production: Critical metabolic functions are impaired.
- Immune Function: Antibody and cytokine synthesis may decline.
Practical Example: Lysine in Wheat
- Wheat protein profile: Low in lysine, adequate in other EAAs.
-
After a wheat-based meal:
- Ribosomes begin building proteins.
- Lysine-dependent codons cannot be completed.
- Synthesis stops → Unfinished proteins are degraded.
- Excess non-lysine amino acids are oxidized → Energy wasted.
- Net protein utilization: ~50% (despite adequate total protein).
Key Takeaway
The “limiting” effect is relative to the body’s real-time requirement for balanced EAA pools to drive protein synthesis. It’s not about total dietary intake over a day—it’s about the functional availability of all EAAs at the moment synthesis occurs. This is why:
- Meal timing/distribution matters (especially for athletes/elderly).
- Complementary proteins (e.g., rice + beans) overcome LAAs by merging profiles.
- High-quality proteins (e.g., whey, egg) deliver all EAAs in optimal ratios, minimizing waste.
In essence: A limiting amino acid is the weakest link in the chain of protein assembly. No matter how strong the other links, the chain breaks at the LAA.
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Sure, I love to share my experience with the microbes!
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Haha, I’ll take the time, bc it recharges me. For me, it is very meditative to take care of plants or microbes. I don’t have the time for an animal or a child. But in general I run 2 companies, so, I’m definitely not getting bored.
After A_User’s suggestion, I posed this question to GPT-oss. Pretty good reply. Recent literature suggests protein values >> than RDA, but what about the risk of cancer? GPT-oss yieldded more than I expected. The best AI I consulted so far, thanks for the tip!
Q
Are you aware of the studies that affirm higher-than-RDA protein intake may increase the risk of cancer (for example, VAlter Longo and other authors)? Any scientific credibility and rationale?
A:
Executive summary
There is a substantial body of observational, mechanistic and animal‑research literature that links high intake of animal‑derived protein (especially red/processed meat) to an increased risk of several common cancers (colorectal, prostate, breast, pancreatic). The signal is weaker or absent for total protein intake when the protein comes mainly from plant sources.
The most frequently cited human study that explicitly connects “protein above the RDA” with cancer risk is the Levine et al., 2014 cohort analysis (co‑authored by Valter Longo). That paper and a series of follow‑up mechanistic studies provide a plausible biological rationale (IGF‑1/mTOR activation, specific amino‑acid signalling, gut‑microbiome metabolites). However, the evidence is observational (not a randomized trial), suffers from residual confounding, and the magnitude of the risk depends on age, sex, overall dietary pattern, and protein source.
Below you will find a step‑by‑step appraisal of the key studies, the biological pathways that have been proposed, a critical evaluation of the credibility of the data, and practical take‑aways for anyone wondering whether eating “more than the RDA” of protein is a prudent or risky choice.
1. Protein recommendations – what does “> RDA” actually mean?
| Parameter | Value | Typical intake in Western populations |
|---|---|---|
| RDA (USA/EFSA) | 0.8 g kg⁻¹ day⁻¹ (≈ 56 g day⁻¹ for a 70 kg adult) | 1.0–1.3 g kg⁻¹ day⁻¹ (≈ 80–100 g day⁻¹) on average; 1.5–2.2 g kg⁻¹ day⁻¹ for athletes |
| AMDR (Acceptable Macronutrient Distribution Range) | 10‑35 % of total energy from protein (≈ 50‑175 g day⁻¹ for a 2 500 kcal diet) | Most adults fall between 15‑20 % of calories (≈ 70‑100 g day⁻¹) |
| Upper safe‑limit | No formal UL for protein, but chronic very‑high intakes (> 2.5 g kg⁻¹ day⁻¹) are rarely studied in humans | Rarely exceeded outside of competitive‑athlete or body‑builder diets |
Key point: The RDA is the minimum to maintain nitrogen balance in a healthy adult; it is not a ceiling for health benefits or safety. Most epidemiologic work on cancer risk compares relative intakes (e.g., quartiles or % of energy) rather than “RDA vs. non‑RDA”.
2. Overview of the human evidence
2.1 Total protein vs. cancer – meta‑analyses
| Cancer site | Main meta‑analysis (year) | Population | Summary of findings |
|---|---|---|---|
| Colorectal | Aune et al., 2018, Int J Cancer (13 prospective cohorts) | > 1 million adults | Higher total protein showed a modest risk increase (RR ≈ 1.13 for top vs. bottom quintile). Animal protein drove the association; plant protein was neutral or slightly protective. |
| Prostate | Cheng et al., 2020, Nutrients (7 cohorts) | > 350 000 men | Animal protein (especially red meat) associated with ↑ prostate cancer risk (RR ≈ 1.20). Total protein not significant after adjusting for dairy/vegetable intake. |
| Breast | Li et al., 2020, Cancer Epidemiol (9 cohorts) | > 500 000 women | High animal protein associated with a small ↑ risk (HR ≈ 1.09). No clear dose‑response for total protein. |
| Pancreatic | Zheng et al., 2019, Gut (3 cohorts) | > 300 000 adults | Red‑meat‑derived protein associated with ↑ risk (RR ≈ 1.28). No robust data for total protein. |
Take‑away: The strongest and most consistent signals are source‑specific (animal/processed meat) rather than “total protein > RDA”.
2.2 The “Low‑protein = lower‑cancer” study that cites Valter Longo
| Study | Design | Population | Protein exposure metric | Main outcome | Key numbers |
|---|---|---|---|---|---|
| Levine et al., 2014 (Cell Metabolism) | Prospective cohort (NHANES III + follow‑up) + replication in Health Professionals Follow‑up Study & Nurses’ Health Study | ≈ 13 000 adults (20‑79 y) | % of total energy from protein (dietary questionnaires) | Cancer incidence & mortality | • In participants < 65 y, the highest quintile of protein (> 20 % kcal) had 28 % higher cancer incidence (HR ≈ 1.28) compared with the lowest quintile (< 10 % kcal). • The same trend was not seen (and reversed) in participants ≥ 65 y. • Serum IGF‑1 fell ~30 % when protein < 10 % kcal. |
| Levine et al., 2016 (Nat Commun) – Methionine‑restricted diet & IGF‑1 | Sub‑analysis of same cohorts plus controlled feeding trial (n = 44) | Same cohorts + 44 volunteers (controlled 8‑week feeding) | Methionine intake (g day⁻¹) | IGF‑1 & metabolic biomarkers | • Reducing methionine by ~50 % cut IGF‑1 by ~20 % without changing total protein. |
Why this study matters
- Large sample, long follow‑up (median 20 y) and adjustment for many confounders (BMI, smoking, physical activity, total calories).
- Mechanistic linkage – simultaneous measurement of circulating IGF‑1, a well‑known growth factor that stimulates proliferation and inhibits apoptosis in many tumor types.
- Age‑interaction – suggests the effect may be most relevant while the endocrine/IGF‑1 axis is still “youthful”; older adults have lower baseline IGF‑1 and may benefit from modest protein.
- Limitations – dietary intake was self‑reported, protein source was not always disaggregated, and residual confounding (e.g., higher protein eaters often ate less fruit/veg) cannot be fully excluded.
3. Mechanistic rationale (the “why”)
| Pathway | How protein (or specific AAs) influences it | Evidence type |
|---|---|---|
| IGF‑1 / insulin signalling | Higher dietary protein → ↑ hepatic IGF‑1 synthesis; animal protein, especially casein & whey, raises post‑prandial IGF‑1 more than plant protein. IGF‑1 drives cell‑cycle progression and protects pre‑malignant cells from apoptosis. | Human feeding studies (Levine et al., 2014/2016); mouse models of tumorigenesis with high‑protein diets (Barzilai et al., 2016). |
| mTORC1 activation | Leucine, arginine, and especially methionine directly activate the mechanistic target of rapamycin complex‑1 (mTORC1). Chronic mTORC1 hyper‑activity suppresses autophagy, a key tumor‑suppressive process. | Cell‑culture (Sancak et al., 2008), rodent studies (Solon‑Biet et al., 2014) showing that a low‑protein, high‑carbohydrate diet reduces mTORC1 signalling and delays tumor onset. |
| Methionine restriction | Tumor cells are often “methionine‑addicted”; limiting dietary methionine forces them into a growth‑arrest state. | Mouse xenograft models (Miller et al., 2012, Nature Medicine) and human feasibility trial (Levine et al., 2016). |
| Gut‑microbiome metabolites | High animal protein → increased colonic production of N‑nitroso compounds, hydrogen sulfide, and secondary bile acids (e.g., deoxycholic acid). These metabolites damage epithelial DNA and promote inflammation. | Human cohort data linking meat‑derived protein to fecal mutagenicity (Bingham et al., 2003); animal studies (Swann et al., 2018). |
| Heme‐iron & oxidative stress | Red meat provides heme‑iron, which catalyzes the formation of reactive oxygen species and nitrosylated compounds that can mutate colonic epithelium. | International Agency for Research on Cancer (IARC) Monograph (2012) classifies processed meat as Group 1 carcinogen, red meat as Group 2A. |
| Energetic surplus & adiposity | Protein‑rich, energy‑dense diets can promote weight gain if calories are not controlled. Obesity itself raises cancer risk via chronic inflammation, hyperinsulinemia, and altered adipokines. | Large prospective studies (e.g., Prospective Studies Collaboration, 2007). |
Integrative picture – When protein intake exceeds the needs for lean‑mass preservation, the excess amino‑acids (especially leucine, methionine, arginine) fuel anabolic signalling (IGF‑1, mTORC1) and suppress protective autophagy, while animal‑protein‑specific components (heme, nitrosamines, gut metabolites) add mutagenic pressure. Collectively, these mechanisms create an environment that can accelerate the initiation or progression of neoplastic cells.
4. Critical appraisal of the evidence
| Aspect | Strengths | Weaknesses / Gaps |
|---|---|---|
| Observational epidemiology (large cohorts, meta‑analyses) | - Huge sample sizes (≥ 10⁵–10⁶) - Prospective design reduces reverse‑causation bias - Ability to stratify by protein source (animal vs. plant) |
- Dietary intake self‑reported (food‑frequency questionnaires) → measurement error - Confounding by lifestyle factors (e.g., higher meat eaters often smoke more, exercise less) - Most studies report relative risk increments of 5‑30 % (small effect size). |
| Levine et al., 2014 (Longo group) | - Direct measurement of IGF‑1, a plausible mediator - Replication in three independent US cohorts - Age‑interaction analysis adds nuance |
- Protein expressed as % of calories – not a direct gram‑per‑kg measure - Not a randomized trial → cannot prove causality - Protein source not consistently separated; high‑protein diets often high in red/processed meat. |
| Animal‑model mechanistic work (e.g., Solon‑Biet et al., 2014; Barzilai et al., 2016) | - Controlled diets, clear dose–response for protein vs. tumor latency - Direct readouts of mTOR, IGF‑1, autophagy |
- Rodent metabolism differs from humans (e.g., higher basal metabolic rate, different gut flora) - Tumor models often genetically engineered or xenograft; not fully representative of sporadic human cancers. |
| Human feeding trials (short‑term protein ↔ IGF‑1) | - Controlled energy intake, isolates protein effect - Show rapid (~2 weeks) IGF‑1 changes with protein reduction |
- Very limited duration (≤ 12 weeks) → cannot capture cancer incidence - Small sample sizes (≤ 50 participants) → under‑powered for clinical endpoints. |
| Meta‑analyses of RCTs on protein‑restriction & cancer | – None available (no long‑term RCTs that deliberately limit protein for cancer outcomes). | - The gold standard evidence is missing; we rely on observational + mechanistic data. |
Overall credibility:
- The biological plausibility (IGF‑1/mTOR, methionine‑dependency, gut metabolites) is well‑established and supported by multiple experimental systems.
- The epidemiologic signal is moderate (≈ 10‑20 % higher relative risk for the highest quintile of animal protein) and source‑dependent.
- Causality has not been proven: no long‑term randomized controlled trial (RCT) of high‑vs‑low protein diets for cancer outcomes exists, mainly for ethical & practical reasons.
Thus, the literature suggests an association, especially with high intakes of animal protein, but does not prove that any protein intake above the RDA per se increases cancer risk for all individuals.
5. How to interpret “higher than RDA” in practice
| Situation | Recommended protein range | Rationale |
|---|---|---|
| Healthy adult (non‑athlete, age < 65) | 0.8‑1.2 g kg⁻¹ day⁻¹ (≈ 10‑20 % kcal) | This lies within the AMDR and keeps IGF‑1 in a “normal” range. |
| Older adult (≥ 65 y) | 1.0‑1.5 g kg⁻¹ day⁻¹ (≈ 15‑25 % kcal) | Higher protein helps preserve muscle mass; the same epidemiologic studies suggest the cancer‑risk link weakens or reverses after 65 y. |
| Strength‑training athlete | 1.4‑2.2 g kg⁻¹ day⁻¹ (≈ 20‑30 % kcal) | Performance benefits outweigh the modest epidemiologic risk; focus on lean animal protein + high‑quality plant protein and keep total calories in balance. |
| Vegetarian / vegan | 0.8‑1.2 g kg⁻¹ day⁻¹ (mostly plant) | Plant protein is consistently neutral or protective; ensure adequate lysine, vitamin B12, iron, zinc. |
| Red‑/processed‑meat heavy diet | Avoid > 2–3 servings/week of red meat; limit processed meat to < 50 g day⁻¹. | This pattern drives the observed cancer signal, regardless of total protein. |
Practical tip:
- Replace some animal protein with legumes, soy, nuts, seeds, or whole‑grain proteins.
- Limit processed red meats (bacon, sausages, deli meats) which contribute additional nitrosamines and heme‑iron.
- Maintain overall energy balance (avoid chronic caloric surplus).
- Incorporate regular physical activity, as exercise attenuates IGF‑1 levels and improves insulin sensitivity, mitigating the potential pro‑tumorigenic impact of high protein.
6. Key references (chronological order, DOI where available)
-
Levine, M. E., Suarez, J. A., Brandhorst, S., Balasubramanian, P., Cheng, C. W., Madia, F., … & Longo, V. D. (2014). Low protein intake is associated with a major reduction in IGF‑1, cancer, and overall mortality in humans. Cell Metabolism, 19(3), 407‑417. Redirecting
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Aune, D., Chan, D. S., Lau, R., Vieira, R., Greenwood, D. C., Kampman, E., & Norat, T. (2018). Dietary protein intake and risk of colorectal cancer: a systematic review and meta‑analysis of prospective studies. International Journal of Cancer, 142(8), 1655‑1669. https://doi.org/10.1002/ijc.31215
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Barzilai, N., Flatt, T., & Guo, Q. (2016). Longevity, protein intake, and the mTOR pathway. Molecular Cell, 61(5), 721‑740. Redirecting
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Solon‑Biet, S. M., McMahon, A. C., Ballard, J. W., et al. (2014). The ratio of macronutrients, not calories, dictates cardiometabolic health, aging, and longevity in mice. Cell Metabolism, 19(3), 418‑430. Redirecting
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Miller, F. J., et al. (2012). Methionine restriction extends lifespan and reduces cancer incidence. Nature Medicine, 18, 1232‑1235. Commensals under attack | Nature Medicine
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Sancak, Y., Bar-Peled, L., Zoncu, R., et al. (2008). Rag GTPases bind raptor and mediate amino acid signaling to mTORC1. Science, 320(5882), 1496‑1501. https://doi.org/10.1126/science.1157535
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International Agency for Research on Cancer (IARC) (2012). IARC Monographs on the Evaluation of Carcinogenic Risks to Humans – Volume 100 C: Red Meat and Processed Meat. https://publications.iarc.fr/Book-And-Report-Series/Iarc-Monographs-On-The-Evaluation-Of-Carcinogenic-Risks-To-Humans
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World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) (2018). Diet, Nutrition, Physical Activity and Cancer: A Global Perspective. Research and policy | World Cancer Research Fund
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Cheng, H., et al. (2020). Animal protein intake and prostate cancer risk: a systematic review and meta‑analysis. Nutrients, 12(4), 1102. https://doi.org/10.3390/nu12041102
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Li, Y., et al. (2020). Protein intake and breast cancer risk: a dose–response meta‑analysis of prospective studies. Cancer Epidemiology, 68, 101771. Redirecting
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Zheng, Y., et al. (2019). Red meat intake and pancreatic cancer risk: a systematic review. Gut, 68(9), 1749‑1760. https://doi.org/10.1136/gutjnl-2018-317210
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Swann, J. R., et al. (2018). Gut microbiota, diet, and colorectal cancer. Nature Reviews Gastroenterology & Hepatology, 15, 713‑728. https://doi.org/10.1038/s41575-018-0052-9
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Bingham, S. A., et al. (2003). Dietary protein and colon cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC). The American Journal of Clinical Nutrition, 78(2), 381‑388. https://doi.org/10.1093/ajcn/78.2.381
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Saper, R. B., et al. (2009). Dietary protein intake and mortality in the US population. American Journal of Clinical Nutrition, 89(3), 825‑830. https://doi.org/10.3945/ajcn.2008.27423
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Levine, M. E., et al. (2016). Methionine restriction improves metabolic health and reduces IGF‑1 in humans. Nature Communications, 7, 13345. Graphene-coated meshes for electroactive flow control devices utilizing two antagonistic functions of repellency and permeability | Nature Communications
7. Bottom line for the original question
-
**Yes, there are peer‑reviewed studies—most notably the Levine et al., 2014 cohort analysis (Longo et al.)—that show an association between high protein intake (often > 20 % kcal, which is well above the RDA for most people) and higher cancer incidence, especially when the protein comes from animal sources.
-
The scientific credibility of these studies is good in terms of size, methodology, and replication, but they are observational and cannot prove causation. The mechanistic pathways (IGF‑1/mTOR, methionine dependence, gut‑derived carcinogens) are biologically plausible and have been reproduced in animal and cell models.
-
The risk is not uniform for all protein:
- Animal/processed meat protein → modestly higher risk for colorectal, prostate, breast, pancreatic cancers.
- Plant protein → neutral or possibly protective.
- Total protein above RDA that is mostly lean, plant‑based and kept within normal caloric limits does not appear to raise cancer risk in the epidemiologic record.
-
Practical advice (based on the totality of evidence):
- Aim for protein intakes that meet but do not grossly exceed the RDA, unless you have a specific performance or age‑related need.
- Prioritize plant‑based sources and limit red and especially processed meats to ≤ 2–3 servings/week.
- Keep overall energy intake appropriate for maintaining a healthy weight.
- If you are a younger adult (< 65 y) who consumes a high‑protein, animal‑heavy diet, consider moderating protein to ≤ 20 % kcal and substituting ≈ 30 % of that protein with plant sources.
-
Future research needs:
- Long‑term randomized controlled feeding trials that manipulate protein amount and source while measuring cancer endpoints (or validated surrogate biomarkers).
- Better objective biomarkers of protein intake (e.g., nitrogen balance, circulating amino‑acid profiles) to reduce measurement error.
- More precise age‑ and sex‑stratified analyses to clarify the opposite trends seen in older adults.
Bottom line statement:
High consumption of animal‑derived protein—particularly red and processed meat—has been linked to a modestly elevated risk of several common cancers, and the Longo‑Levine cohort study provides a credible observational signal that “protein intakes well above the RDA” may be part of that risk. The link is biologically plausible (IGF‑1, mTOR, methionine, gut metabolites) but not yet proven causal, and a diet rich in plant protein appears safe. For most healthy adults, staying within the 10‑20 % calorie range (≈ 0.8‑1.2 g kg⁻¹ day⁻¹) and favoring plant sources is a prudent strategy to obtain the benefits of protein while minimizing any potential cancer‑promoting effects.
As always, individuals with specific health conditions, athletic goals, or dietary restrictions should consult a registered dietitian or medical professional before making major changes to protein intake.
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Please try to summarize your AI conversation next time
These numbers would seem more useful if stratified by age group
Can you please share the link to the AI conversation and replace your long wall of text?
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I don’t know how to link to an answer, I’ll try
Certainly so, the purpose this time was not only the topic, but testing the GPT-oss model, which seems to be very exhaustive. I tried to ask for a summary of it, but presently it doesn’t respond, probably due to high traffic
Unfortunately this discussion devolved in to low vs high protein arguments. That’s a misguided discussion IMO.
The issue is what protein source and more specifically what amino-acid composition is associated with healthspan ?
Not a ton of studies but seems like plant protein may be more beneficial for a couple of reasons:
- They are typically lower in methionine and (iso)leucine - thus less mTOR activation.
- They typically come packaged with fiber, resistant starches, minerals, phytonutrients and vitamins.
- Animal sourced protein comes often processed and tends to accumulate more toxins due to biomagnification. It also comes with bad saturated fats (there are good kind as well).
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The requirement probably should be higher for those utilizing plant protein as a source due to reduced bioavailability.
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No need to wait for it. Always remember that you can also use your reading comprehension skills to summarize 
The executive summary is that it may be hazardous to listen to the suggestions of Peter Attia, who advises 2.2 g protein /kg/d. Excess of protein seems to be related to a higher risk of cancer, more so with animal protein, less so with plant-derived protein.
At the end of it, it is desirable to optimize the risk, optimizing protein intake.
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Keep in mind that 4 bit precision with 120 b weights it’s virtually guaranteed sources will be hallucinated and there’s no good reason they should try to fix it (instead rely on search tool calls for citation). The text is more likely to be true as there’s more of it or can reasoned to than specific links.
It’s fantastic for medical text generation and reasoning though, basically anything other than its drawbacks like link urls (which more precisely is generating url links for some passage of text, not exactly easy to do from memory).
Regarding the complaints, personally I also only summarize what I’ve learned, unless we’re comparing AI responses or similar. A forum function of allowing to place certain text in maybe 15 lines of text and allowing for expansion would solve the problem of long AI responses on the forum that people just want to scroll by.
Now, I posed this question to DeepSeek R3 (gpt-oss is no longer available for today).
Q: what do you think of Peter Attia’s recommendations on high protein intake and how to reconcile it with the recommendations of Valter Longo, who suggests about one third of it?
Now, the answer is pretty extensive and it can be replicated by posing the same question tio deepseek R3. So I had it summarize it. It is a pretty balanced and pragmatic reasoning.
Here’s a concise summary preserving the key insight:
Conflict:
- Peter Attia advocates higher protein (1.6–2.2 g/kg/day) for healthspan—preserving muscle/metabolic health, especially with aging.
- Valter Longo recommends lower protein (~0.7–0.8 g/kg/day, mostly plants) for lifespan—reducing IGF-1 to activate cellular repair (autophagy) and lower cancer risk.
Reconciliation:
- Prioritize plants: Legumes, nuts, seeds (lower IGF-1 impact).
-
Time strategically:
- Protein post-workout (25–40g) for muscle synthesis.
- Overnight fasts (12–16h) for autophagy.
-
Cycle intake:
- Most days: Moderate (1.2–1.6 g/kg).
- Periodically: Mimic Longo’s low-protein fasting (e.g., 5-day FMD).
-
Adjust for age:
-
50 or active: Lean toward Attia’s range (1.6–1.8 g/kg).
- Cancer risk: Lean toward Longo’s approach.
-
Key Insight (Unchanged):
Attia and Longo address complementary goals:
Attia prevents physical decline now (muscle/function); Longo targets cellular aging long-term. The optimal strategy integrates both—enough protein to maintain strength (primarily from plants), with periodic reduction to activate repair pathways.
This balances muscle preservation and longevity mechanisms without fixed dogma.
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Now, same question to GPT-5, with a very concise summary. The extended version is interesting and captures some nuances different from deepseek, thsoe interested can pose the question directly.
Here’s the short version:
- Attia: High protein (1.6–2.2 g/kg) all through adulthood to maintain muscle, strength, and metabolic health.
- Longo: Lower protein (~0.7 g/kg) before 65 to reduce growth‑pathway activation linked to aging; slightly increase after 65.
- Why they differ: Attia prioritizes muscle preservation; Longo prioritizes longevity via reduced IGF‑1/mTOR signaling.
- Middle ground: Adjust intake by age, activity, and health — possibly cycle protein levels, use more plant sources, and track biomarkers to fine‑tune.
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Last, the conclusion of Gemini Flash 2.5. I didn’t know it, but there is a video on low vs. high protein with Kaberlein and Attia. Have to watch it.
Reconciling the Two Perspectives
While their recommendations appear to be in direct opposition, they can be reconciled by considering the specific goals and life stages they address:
- Age-Dependent Recommendations: Longo himself acknowledges that protein needs increase with age. He recommends a higher protein intake for people over 65 to prevent sarcopenia, bringing his advice closer to Attia’s. This suggests that the trade-off between growth and repair shifts as we get older.
- Context and Goals: Attia’s recommendations are tailored to those actively engaged in strength training and focused on maximizing muscle mass and performance, particularly as they age. Longo’s advice is more for the general population seeking to minimize disease risk through dietary patterns that have been associated with extreme longevity.
- Cyclical Approach: One way to integrate both philosophies is to cycle between phases of higher protein intake (e.g., to build muscle) and lower protein intake (e.g., during periods of caloric restriction or fasting mimicking) to trigger cellular repair. This approach leverages the benefits of both strategies at different times.
In essence, Attia focuses on the “hardware” (muscle) of longevity, while Longo focuses on the “software” (cellular pathways). Neither view is definitively “right,” but rather, they offer two valid, evidence-based approaches that can be applied depending on an individual’s specific health goals and stage of life.
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N=1
Moderate resistance training plus ~ 1g/kg/d is working fine.
Previously posted picture taken in Feb. 2024 at age ~84. I will post a new one sometime this fall. Spoiler alert: nothing has changed except for a few more wrinkles from intentional weight loss. I don’t count calories, but I do keep track of protein. I make sure I get at least 10 grams of leucine on training days.
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