Computational Aging

While there are tailwinds for aging science, there are also many unanswered questions, barriers and headwinds.

Here are a few issues:

1. Lack of understanding of root cause of aging. We have 9+ hallmarks of aging, but what drives aging? Until we have a clear answer on the root causes, we will be shooting in the dark.
2. Decades of effort on aging science with no "life extension" drugs on the market. The Mediterranean diet might be the closest thing we have so far. 
3. Decades of effort on Alzheimer's with little to show for it. Alzheimer's is narrower than aging and therefore seems like it should be easier to make progress on. Yet, Alzheimer's patients have few options.
4. Slow progress in model organism life extension. There are examples, mostly of marginal life extension in lower organisms, but we aren't seeing mice live for 2x their typical lifespan. (Encouragingly, a June 2022 study in Nature Communications Biology showed Drosophila melanogaster living to >200 days, a 120% increase vs. average lifespan, with a combination of interventions [1].) 
   
5. Not a national/global priority. Only a tiny sliver of our population works on aging science.
6. How do you run clinical trials? Healthy humans live to be 80+. How do you prove an intervention significantly extends lifespan in a reasonable amount of time?
   

These issues raise concerns about our ability to make progress on aging science, especially if we want significant lifespan and healthspan extension in the next 40 years (which is my goal). Most importantly in my view, we need to get clear on the drivers of aging at the molecular, cellular, tissue and organism level. Once we know the drivers / root causes, it will make it easier to create therapies.

[1] Shaposhnikov, M.V., Guvatova, Z.G., Zemskaya, N.V. et al. Molecular mechanisms of exceptional lifespan increase of Drosophila melanogaster with different genotypes after combinations of pro-longevity interventions. Commun Biol 5, 566 (2022). https://doi.org/10.1038/s42003-022-03524-4

Aging science is incredibly hard and complex. However, I believe we are poised for stunning breakthroughs in the next 40 years, driven by three tailwinds for aging science.

First, consider the aging population globally, which will bring increasingly intense pressure, interest and resources to find solutions. The UN predicts that the over-65 population will more than double from 2019 to 2050, to 1.3 billion people [1]. The UN also predicts the over-80 population will nearly triple from 2019 to 2050, to 426 million. Aging people will push for progress against age-related disease. They will vote for more funding. They will donate their money and effort to help. This is already happening. Witness the rapid growth of new aging science centers and labs, and startups working on longevity. It's not hard to imagine 10x the number of people working on aging science in 2030 vs. a few decades ago.

Second, consider how much foundational work has been done in the last few decades to understand aging at the molecular, cellular, tissue and organismal levels. 1000s upon 1000s of studies to understand the hallmarks of aging, the mechanisms of age-related diseases and the success of countless interventions. This base of knowledge means we can stand on the shoulders of giants. For example, in the last 30 years, we now understand what senescent cells are, how they impact aging and how to remove them (via senolytics). These could be FDA-approved medicines in the next decade. As another example, we identified that "parabiosis" (blood-sharing) rejuvenates old mice with young mice blood, and that the mechanism is likely clearing out molecular "noise" [2]. These benefits appear to come from plasma exchange that doesn't even require any "young blood". Once again, this could be an FDA-approved therapy in the next decade. 

Third, consider a few fundamental breakthroughs, including genomics, cellular reprogramming, CRISPR and artificial intelligence. These are general purpose technologies that are creating revolutions in biological research and beyond. The progress in single-cell genomics is leading to exponential growth in data and insights into our cells. The ability to reprogram cells, originally via "Yamanaka Factors," is leading to a flurry of new research and heavily funded startups. For example, we can now program astrocytes into functional dopamine neurons, and even reverse a model of mouse Parkinson's [3]. CRISPR drastically simplifies gene editing, which is a boon for research and for new treatments. The progress in deep learning even in the last 5 years is night and day. These breakthroughs are being used together, and are building on each other. Labs and startups are using genomics, reprogramming and AI together to do things that would have been impossible 5 years ago.

Together, these tailwinds set the table for what I believe will be astounding progress in aging science in the next 40 years.

For example, let's do a quick thought experiment: imagine that going forward, we find just one new thing that extends lifespan/healthspan by 10 years in each decade. In four decades, your life expectancy could be 40 years longer than you thought.

[1] United Nations Department of Economic Social Affairs. World population prospects 2019: highlights. New York: United Nations Department of Economic Social Affairs; 2019.

[2] Kim, D., Kiprov, D.D., Luellen, C. et al. Old plasma dilution reduces human biological age: a clinical study. GeroScience (2022). https://doi.org/10.1007/s11357-022-00645-w

[3] Qian, H., Kang, X., Hu, J. et al. Reversing a model of Parkinson’s disease with in situ converted nigral neurons. Nature 582, 550–556 (2020). https://doi.org/10.1038/s41586-020-2388-4

If we want to make progress against aging and age-related disease, we should define what these are.

There is considerable debate around defining aging. A 2018 review paper notes, "Nowadays one of the most crucial questions of the biological aging research is to determine what is aging per se" [1].

One way to define aging is lifespan and death rates by age. One main reason aging can be terrible is that your risk of death grows exponentially.

This figure, based on mortality data of Japanese women, shows that your likelihood of death increases exponentially with age [2]. Thus, one way to define aging is lifespan and death rates by age. Progress would mean increased lifespan and decreased death rates by age.

But what are the diseases associated with death? We can look at CDC-reported causes of death.