Computational Aging

In 2022, I started earnestly working in biomedicine after over a decade in tech and startups. As the year nears a close, I wanted to reflect on my work and the general outlook for aging science.

Is aging science the right focus? My big goal is that humanity achieves significant lifespan and healthspan extension by 2063. I decided to direct my professional energy to this, while maintaining a high quality of life otherwise. To be clear, It does not mean sacrificing today's life for some future life. Overall, I still think this is a great goal. By 2050, 1.3 billion people will be over 65 years old [1] and likely starting to suffer from age-related decline and disease. It would be great personally, for my family and friends, and for billions of people around the world to have more healthy years.

Is the goal realistic? At a high-level, yes. We know that biology figured out how to reproduce young cells and organisms from old cells via reproduction. There are "negligibly senescent" creatures like the hydra, ocean quahog, Galapagos tortoise, and naked mole rat. I've been inspired again and again by new discoveries this year. Increasingly, it seems that cellular reprogramming can reduce the functional age of cells and tissues without causing cancer. Cellular reprogramming on its own could be one of the defining technologies for the 21st century. The advances in machine learning have been stunning, from AlphaFold to Chat-GPT. These will increasingly be applied to biomedical discovery. Of critical importance, there is no shortage of funding for this work in the foreseeable future. The aging population, which happens to hold most of societal wealth, will continue to invest more and more money into biomedicine, which will attract new talent and ideas, and encourage faster progress. There will be millions and millions of brilliant people around the world working on aging science.

What should the next decade look like for aging science? We still haven't proven the fundamental science. There are increasingly powerful theories of aging (e.g. Sinclair's "information theory"), but we still have open questions of what drives aging. Cellular reprogramming is incredibly exciting, but it still needs to be proven to safely extend long-term lifespan in other organisms, let alone humans. Priority #1, in my view, is to progress fundamental aging science as quickly as possible, including cellular reprogramming. Priority #2 should be bringing discoveries from the last few decades to drugs that can moderately extend healthspan and lifespan. Senolytics are compelling for moderate life extension, but we don't yet have proven drugs. Young blood plasma is surprisingly powerful, yet we don't have safe therapies. Rapamycin, metformin, and new diet and exercise interventions are all currently in clinical trials. We need interventions like these to start hitting the market in the next decade, which will extend healthspan and build momentum for the field. Priority #3 should be exploring new, risky ideas for aging science, beyond the current mainstream. How do other organisms achieve negligible senescence? How can advances in machine learning and large-language models reveal insights into aging?

I'm thrilled to be working in this field in an actual paying job. The learning curve is steep. I hope to contribute by my own efforts in science. I also hope to inspire others to switch their careers into aging science.

References:

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

After over a decade in startups, I recently joined UCSF’s Abbasi Lab. I began in February 2022 as a volunteer, and then I went full-time in September 2022 on a one-year specialist appointment. It has been a wonderful experience so far, and I wanted to share why.

My big goal and hope is that humanity significantly slows or even reverses aging in the next ~40 years. In exploring how to have maximum impact, I am pursuing the computational / data science path vs. the wet lab / biological sciences path. It's a better fit personally. Biological data collection and machine learning are each improving rapidly, likely exponentially. I learned from the startup world that it’s good to be a part of things that are improving exponentially. The marriage of the two will lead to amazing discoveries in the coming years. In my work, I hope to build better ways to measure human aging. With better measures, we can more quickly figure out how to slow aging. Abbasi Lab was a natural fit, given it is a computational and machine learning (ML) lab working on biological data. I also like that Abbasi Lab has a focus on neuroscience––if we can’t slow brain aging, then it probably won’t be worth having longer lifespans.

The work is cutting edge and highly relevant to aging science. My focus is currently applying machine learning to spatial transcriptomics (ST) data. ST allows measurement of the expression (transcripts) of 1000s of genes in their native spatial location at single-cell resolution. In 2021, ST was named “Method of the Year” by Nature Methods [1] because of the potential for spatially-resolved gene expression data to unlock new secrets about biology, development, disease, and aging. The sheer size and rapid growth of ST datasets requires ML to make sense of it. Like ST, ML has seen incredible method development in recent years. ML is beginning to solve previously-flummoxing biological problems, such as Google’s AlphaFold for protein folding [2]. However, ML can be especially challenging to apply to biology, as the results from ML models need to be accurate, repeatable, and interpretable to biological reality [3], [4]. Thus, we are developing accurate, repeatable, interpretable ML tools and frameworks to help biologists explore and analyze ST data.

Other Abbasi Lab projects are also breaking new ground at the intersection of biological data and ML. One of our team members is creating a deep learning system to take medium resolution MRI images and automatically turn them into high resolution MRI images, which can more accurately measure the progression of brain disease and aging. Another is building an ML system to diagnose severity of Parkinson’s Disease using only brief videos of patients walking. One that I am particularly excited about is an effort to measure brain disease and related functional phenotypes based on dozens of physical sensors on participating patients. Together, I see the insights from this work will lead to new, automated ways to measure aging in real-time. Once we can measure aging in real-time, we will be able to test new interventions and therapies at record speed.

Abbasi Lab is in the center of the action at UCSF. It offers incredible access to data and willing patients, and some of the most talented researchers and clinicians in the world. Our lab space is on the top floor of a gleaming new building, the UCSF Joan and Sanford I. Weill Neurosciences Building (pictured below).

Located in UCSF’s Mission Bay Campus in San Francisco, we are across the street from Chase Center and nestled among a number of other UCSF research buildings and facilities. Good coffee, food, and outdoor space are abundant, with easy access to public transportation. Biotechs and VCs are literally steps away, making it easier for discoveries to get to market.

Reza Abbasi-Asl is the Principal Investigator and leader of Abbasi Lab. Reza’s talents are what makes this possible. He draws talented graduate students and postdocs. He has high expectations. He takes the time to explore, discuss, debate, and coach. He exudes the contagious energy of a lab and researcher on the upswing. We’re developing a fun, synergistic culture working on a range of interesting topics. We expect to recruit many new talented, interdisciplinary researchers to join our efforts.

In the end, I’m here at Abbasi Lab because I see us building foundational tools for the fight to slow aging and offer humanity many more healthy years.

References:

[1] Marx, V. Method of the Year: spatially resolved transcriptomics. Nat Methods 18, 9–14 (2021). https://doi.org/10.1038/s41592-020-01033-y

[2] Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2

[3] B. Yu and Karl Kumbier, “Veridical data science,” Proceedings of the National Academy of Sciences, 117 (8) 3920-3929, Feb. 2020, doi: https://doi.org/10.1073/pnas.1901326117

[4] W. J. Murdoch, C. Singh, K. Kumbier, R. Abbasi-Asl, and B. Yu, “Definitions, methods, and applications in interpretable machine learning,” Proc. Natl. Acad. Sci. U. S. A., vol. 116, no. 44, 2019, doi: 10.1073/pnas.1900654116

To try to slow my own aging, I made three lifestyle changes in the last year:

• Diet: I switched to primarily vegan, in addition to not eating breakfast until 10-11am most days. Along with many other studies, a 2022 meta study suggested a vegan diet is associated with ~8-13 years of longer life than an average western diet [1]. I figure a mostly vegan gets me 80-90% of that benefit. After making this change, my total cholesterol went from consistently high (~220 mg/dL) to consistently in the "healthy" range (~150 mg/dL).
• Daily exercise: I try to get 60+ minutes of exercise as measured by Apple Watch every day. This includes brisk walking. I get there probably ~80% of the days. Previously, I was probably reaching that mark only ~50% of the days. Study after study shows exercise to have broad health benefits, though it's still unclear exactly what types of exercise, when and in what quantities is optimal.
  
• Regular weight lifting: This year, I tried to lift weights at least once per week. I may increase this to twice per week. Many studies show weight lifting can preserve muscle strength, bone density and balance, which are all important for long-term health and safety. I know I'm not as strong as I was when I was in college and lifting weights and playing a lot of ultimate frisbee, but beyond that it's hard to know exactly how well I'm doing on muscle and bone health. 
  

I would guess these changes could extend my healthy lifespan by ~3-5 years, given I already had a healthier lifestyle than the average American.

Still, these are blunt instruments. I am in the dark about how my body is aging. What lifestyle changes or other interventions can extend my healthy lifespan by 10+ years? How are my different organs aging? Where am I losing resilience? What has caused my aging to speed up? To slow down? I would like a more robust, affordable aging measurement regimen. I don't have insight into real-time molecular and tissue changes going on inside my body. Most physiological measurements are lag indicators. For example, the commonly-used frailty index is the result of many systems degrading over time.

Here are three possibilities to greatly improve aging measurement:

1. Near real-time omics measurements. Ideally, I'd have omics measurements taken multiple times a day to measure cellular and molecular dynamics. Today, it costs ~$500 to get a single microbiome omics assessment from Viome or a single DNA methylation age from Elysium. If you did these each 3 times a day for a whole year, it would run you over $1 million. How could we get this to something affordable for every American, e.g. $50/month, like the average cost of a cell phone bill?
2. Frequent imaging. In the future, I'd like a regular full-body, high-resolution imaging of my body to detect aging and disease. Maybe daily or weekly, if we can make it easy? MRI technology can safely image your entire body at high-resolution. It can detect spatiotemporal dynamics and the onset of disease (e.g., multiple sclerosis via brain legions). Companies like Prenuvo and Q Bio are going down this path. Unfortunately, the cost is still high (~$2K/scan) and the process of driving to a facility is inconvenient.
3. Longitudinal comparisons to millions of other people. Once you have all the data from #2 and #3, we need to know what the measurements imply for aging and health. To do this, we need longitudinal comparisons across millions of other people. Will  biobanks will move fast enough to collect this data? Will Apple Health with the Apple Watch will find ways to correlate its troves of real-time physiological to cellular and molecular dynamics?