What does it look like to take aging seriously?
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What does it look like to take aging seriously?

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What does it look like to take aging seriously? - Part I

Status: Draft Rant about how we are fooling ourselves Better summarized in 💊There Is No Cure

After attending a broad range of conferences in the aging space it seems that very few people are actually taking the idea seriously. Maybe this post still reflects my naivete, or a “the king has no clothes” moment. Here are some observations:

  • No one with a good BMI has made it to 140.
  • No one eating organic food or following Ayurvedic practices has made it to 140.
  • No one doing a lot of exercise or being socially active has made it to 140.
  • No one taking currently existing drugs or supplements has made it to 140.
  • No one who never got cancer or Alzheimer's has made it to 140.
  • No one tracking their health or biological age consistently has made it to 140.
  • No one with peak LDL blood levels their entire life has made it to 140.
  • No NIA grant has led to a discovery that made people 140.
  • The likelihood of making it to 140 has not changed a bit since the invention of medicine, peer review, statistics or the PhD.
  • Bowhead whales live to 200, trees to 10,000.

Some more observations:

  • If you just take the controls of mice lifespan studies and overlay them onto other studies - all effects vanish. Rapamycin doesn’t even extend lifespan in mice!
  • Less than 0.01 % of GDP is spent on aging - and most of that on the above interventions.
  • Many species are immortal, especially single-celled or simpler ones. Aging only really occurs in us weird mammals or other highly complex networks like ant colonies.

You can do with that information what you want, but I think there are two possible conclusions:

  1. Common sense / the market is correct: aging is very hard to solve in humans - the Brian Johnson crowd will ultimately achieve nothing, except a little more “health” at the end of life (Both my grandmas made it past 90 in a state of “health” - if we achieved this for everyone on the planet it would be a massive failure)
  2. Aging is solvable, but not with current approaches

The first option is most likely, but what would it look like to assume the second and take it seriously? How would you act if aging is solvable, but no one is trying?

But what are the current approaches that are failing? I would call it “incremental optimization”, acting as if solving aging is similar to increasing transistors on a chip or reaching farther with a plane. You just need to find a good measurement and then unleash the force of humanity. But this fails: any measurement to be optimized needs to know what success looks - and we do not have this in biology. You cannot solve aging by using a biomarker as a KPI and then optimizing for its value, simply because we have nothing that works, so we can not train it on interventions. To repeat: a “biological clock” would only be useful if you could quickly tell whether or not something would extend maximum lifespan - but even if you build a proper clock (one only trained on athletic centenarians - sick or obese people are dying because they are sick or obese!) you couldn’t make it a proper surrogate, simply because we don’t know what it means to stop / delay / reduce aging.

  • Conclusion 3: Solving aging is like inventing the airplane or the Von Neuman architecture, the thing that unleashes optimization.

If this is true what do you need:

  1. Look at the real world: Understanding of how breakthroughs happen and why current systems produce little of it
  2. Finding people with extreme excellence and understand their needs
    1. This contains a lot of detail:
      1. Where are they physically? Can you bring them together across borders?
      2. Can you provide them with maximum support in all aspects of life - including playing status games (money, H-index) for them - so they can focus
      3. Can you get a critical number so they are amongst
  3. Get resources to solve those needs - understand why people with resources give them away
    1. What people are interested in financing actual science? What incentive landscapes are needed?
  4. Can aging be solved by a small team or do you need to build a pipeline / portfolio / institute?
  5. If you need something larger, how can you create a culture of maximum criticism? Especially around pointing out “Failing, even if you succeed”.

[this is actually one of the most important concepts to understand: most things fail not because they are not successfully attempted, but because the wrong thing is attempted. Best example is cancer research: even if you succeed in killing a specific type of tumor, you have failed in three ways: a) when cancer becomes malignant, you are dealing with evolution, so you have just eliminated one rare species - this will unlikely transfer or last b) many species don’t get cancer, so prevention is the thing you should have looked at first, c) the most likely outcome for cancer is not death / remission / cure, but another disease of aging (Alzheimers, etc.). This - a culture of pointing this out early - is currently the biggest blocker to the field: a concrete example, people are excited about organ replacement (just put young stuff in), but we don’t yet know where aging is. Given that it could be local to the brain - we don’t even know if replacing the entire body would achieve any neuroregeneration - this seems premature]

  1. What is the minimum equipment needed to solve this issue quickly? How do you avoid mismanagement of funds while allowing scientists to operate at maximum velocity?
  2. How can you do this without media involvement (given that most ethics are just a social speedbump rather than based in morality) but still reach the right people. Bascially how do you build a high trust network?
  3. Untrain experts that have a lot of valuable tacit knowledge but “optimizer mindset” (write papers / use statistics) and train quacks to be actually useful
  4. How do you figure out the right experiments to run? Which are crucial early nodes in the tech tree? Which hypotheses can you rule out fast?
  5. How do you not fool yourself at any moment?