Now published in PNAS, check out my work from UCSF: Unsupervised pattern identification in spatial gene expression atlas reveals mouse brain regions beyond established ontology: https://www.pnas.org/doi/10.1073/pnas.2319804121https://lnkd.in/dEB37EkQ

We combine stability-driven nonnegative matrix factorization with spatial correlation analysis to explore brain regions and ontology in 3D spatial gene expression profiles.

Our findings reveal a gene expression-defined anatomical ontology and interpretable region-specific genetic architecture captured by the marker genes and spatial co-expression networks in the mouse brain.

This shows the power of genomics + machine learning to create biological maps. As the datasets grow and the algorithms improve, I expect this kind of work will lead to a whole generation of medical breakthroughs.

Code available here: https://github.com/abbasilab/osNMFhttps://lnkd.in/dtymd4gC

Great collaboration with Yu Wang, R. Patrick Xian, Alex Lee in collaboration with the Allen Institute for Brain Science's Hongkui Zeng and Bosiljka Tasic and University of California, Berkeley's Bin Yu. Special thanks to Weill Neurohub and and Sandler Program for Breakthrough Biomedical Research for funding our research.

Many of us are figuring out how to have max impact towards curing aging.

Where are we towards this goal? Generally, humanity has good tactics to live to 70-80 or so in a healthy way. At that point, diseases of age take over, including neurodegeneration, cancer, heart disease, aches and pains, frailty, and much more. There’s a long way to go.

What’s on the horizon? There are 100s/1000s of labs and 100s of companies working directly on aging. These will bring new ideas and medicines to approval. There are far more teams and companies working on enabling tech: genomics, AI, drug delivery, pharma. These will bring new tools to bear on the problem.

What should we do to have max impact in our lifetimes? My answer is to take a big swing on an exciting new technology (non-Yamanaka transcription factors for tissue-specific rejuvenation) and see where it goes. That’s what I’m doing at Junevity, putting all my professional effort and resources to make it happen.

A general answer is to put your time and money in this field, and see where it leads.

Longevity escape velocity is the idea that our life expectancy (and healthspan) increases faster than the passing of time.

Will this happen soon? The more time I have spent in the aging science field the more I am optimistic about our chances for this happening in the next few decades.

There are many tailwinds, and of course headwinds. There are three horizons of anti-aging therapies: horizon 1 are lifestyle improvements already available, horizon 2 are new repair therapies coming soon, and horizon 3 are cellular rejuvenation technologies in development.

AI/ML + big biological data means progress is now exponential. Already, AI/ML is becoming an equal partner in biological research. Soon, it will be the driver.

We are spending more than ever on R&D (2x growth in the U.S. in constant dollars from 2010 to 2021). We are rapidly growing our spend on general venture capital and biotech venture capital. Much of this investment will ultimately go towards helping people live longer and healthier. Our aging population will demand this.

What can we do to bring this future?

I believe it's mostly inevitable, but that we should work to speed it up. To make it happen faster, we need our first anti-aging drug wins. We need FDA approvals for safe, effective drugs that target diseases of aging. Even better, we need rejuvenation therapies (that's what we're working on at Junevity). We need people to see and feel the benefit in their grandparents, parents, and themselves. We need to look in the mirror and see it happening. We need to be inspired. This will drive even more money and talent to create even better rejuvenation therapies.

Stay healthy so you live long enough to participate in this fantastic future. Support R&D funding and startup/pharma development. Invest your time, money, and talent in horizons 1, 2, or 3.

Goals for 2033

1. Look, feel, move, and do activities like I’m closer to 40. In other words, I have dramatically slowed my rate of aging.
2. I know and monitor in near real-time my risk for general aging and each major disease. I know how to balance lifestyle and pharmaceuticals to keep this risk low, with close to real-time updates. As a result of this monitoring and intervention, my risk is very low.
3. I take the first of various FDA-approved therapies to slow and reverse aging for various organs/systems. Eventually there will likely be therapies for muscle, bone, brain, heart, blood, liver, skin, etc.
4. Most people around the world can will be able to do this within another 5-10 years because these technologies are safe, approved by regulatory agencies, accessible, and affordable. 

What do we need to make this a reality?

1. Data-driven real-time monitoring of aging and disease risk
2. Data-driven, personalized real-time interventions to reduce this risk
3. FDA approved therapies for cellular rejuvenation across various tissues
4. Improved fundamental understanding of aging biology

What is the current situation?

• Fundamental understanding of aging biology is rapidly improving. There are probably 100,000s of researchers working on this around the world from various angles. Trends and demographics suggest this will be millions of researchers soon.
• Rapid AI progress + data growth are making data-driven monitoring and intervention in aging much more feasible. Aging science can ride these two exponential curves.
• It is still unclear how much lifestyle changes + known drugs/therapies can slow aging. That said, if I had to guess, known technologies could make average lifespan more like 100-110 if they were rigorously applied with personalized measurement and interventions. But, they will not “rejuvenate” our bodies, or at least not that much.
• FDA approval of rejuvenating therapies is a big question mark. Early clinical trials are started and on their way. The first approvals will really speed this up. We need fast progress here!
• New drugs are typically very expensive. For anti-aging drugs, there will be a strong incentive for governments to subsidize these drugs to reduce the cost burden of age-related disease. Hopefully this will help adoption and affordability.

I posted my first research paper with Abbasi Lab and a great team of collaborators: "Unsupervised pattern discovery in spatial gene expression atlas reveals mouse brain regions beyond established ontology."

Here's the link: https://www.biorxiv.org/content/10.1101/2023.03.10.531984v1.full.pdf)

In this manuscript, we show that unsupervised machine learning (ML) can rapidly create maps of the brain based purely on 3D gene expression data. We demonstrate this in the adult mouse brain that goes beyond established ontology. Existing maps are typically hand-drawn, and can take years of person-hours to complete. By contrast, this method (osNMF) can run in a few hours on a MacBook Pro and is potentially less biased. It is applicable to any tissue or organism. Tissue maps are important. They help us understand function, development, disease, and aging.

This new method is just one of the countless examples of the powerful combination of ML + new data generation methods. Together, they are increasing the pace of biological discovery. Ours was a relatively basic ML approach––and it leads to orders of magnitude improvement in generating tissue maps. This improvements are happening all over the place.

More healthy years is one of the greatest possible gifts.

We have doubled average human life expectancy in the last 150 years. Much of this came from reducing child mortality, as can be seen in the compression of the red/orange/yellow lines in the figure. Excitingly, in the last 50 or so years, we are starting to see gains in older age. The life expectancy for a 70 year old in 1850 England and Wales was around 79 years, which means a 70 year old could expect to live for 9 more years. In 1950, this number hadn't really budged. The tools that improved child mortality (e.g. antibiotics, vaccines, etc) don't help as much for the diseases of old age (e.g. heart disease, cancer, dementia, etc).

Today, with the growing population of older people, the medical system and drug discovery are far more focused on diseases of aging. The life expectancy for a 70 year old in England and Wales today is 86 years. That means we've added ~7 years in 70 years. That's still slow progress (e.g. 1 year gained every decade). It's not enough to help you or me live much longer.

However, you can see that the curve of improvement for older people seems to be showing exponential improvement (I took the liberty of adding the red arrow to emphasize this). Might we be seeing exponential improvement? What could keep this curve exponential? How can we add 30 years of life expectancy for the typical 70 year old in the next few decades? That would be a rate of improvement closer to 1 extra year annually. People have called this "longevity escape velocity", where lifespan/healthspan is growing faster than we can age.

Here's a general framework for how to think about adding 30 healthy years broadly to people around the world

Horizon 1 improvements: Horizon 1 is where we use known science to help people live longer. For example, it appears that the average person could add ~10-15 extra healthy years based purely on lifestyle. This could be as simple as regular exercise (like 30-60 minutes per day, even including brisk walking plus some higher intensity workouts and regular weight training) and a Mediterranean diet. Exercise and diet are amazing because they appear to slow the progression and risk of the scariest diseases, including dementia, cancer, diabetes, and heart disease. Already this is a big industry, but I expect dozens of new $ billion companies to get created simply helping people apply known science to their lives. I also expect the biggest consumer internet companies in the world (Apple, Google, Amazon, etc) to deliver exciting new solutions. Entrepreneurs, go forth! 

Horizon 2 improvements: Horizon 2 is where we apply the learnings in aging biology from the last 30 years to develop new therapies and drugs. These include promising drugs like Metformin and Rapamycin that work well in model organisms and human biomarkers, but haven't been thoroughly tested in humans yet for lifespan extension. They include promising lifestyle interventions like intermittent fasting. They include a crop of new drugs, like BioAge's new sarcopenia (muscle loss) drug that is heading for Phase 2 clinical trials. It also includes the disease-specific progress, such as in Alzheimer's (finally) and cancer. Imagine 5-10 new drug approvals and 1-2 new lifestyle interventions over the next 20 years. If they work on relatively distinct pathways or pathologies, we might get another 10-20 years from these. Maybe everyone over 40 or 50 will be taking a few anti-aging drugs by 2040, e.g. statins for aging? Drug developers, go forth!

Horizon 3 improvements: Horizon 3 are the big, paradigm-changing, rejuvenation technologies. For example, cellular rejuvenation via Yamanaka factors is one of these. They have the potential to rejuvenate our cells and tissues. Horizon 3 improvements are harder to work on. They're risky and uncertain. They may not pay as well. They may fail outright. And sometimes these emerge in surprising ways or places, like Yamanaka factors did. There is tremendous investment going into cellular reprogramming including Yamanaka and other factors. We will learn a lot there. There are other high-risk Horizon 3 possibilities out there too, often in the realm of basic science. It could include replacement organs or tissues grown in a lab. It could include regular blood replacement based on the learnings of parabiosis. It could include riding the exponential improvements in AI to a profound understanding of aging biology. We need people to pursue these. Dreamers and inventors, go forth!

It's hard not to feel optimistic about the future of human longevity. There is tremendous pressure to innovate from our aging population and the growing cost of healthcare. There is a deep human desire to feel and be youthful. Older people, who hold most of the world's wealth, will invest much of it towards health and longevity. Horizon 1 and Horizon 2 have great, growing momentum. They alone may deliver 30 more years of healthy life. Horizon 3 has momentum, but I believe we need more people and money exploring Horizon 3 ideas. A single Horizon 3 technology may bring billions of people decades of more healthy years.

Longevity science seems to be experiencing rapid progress, yet expected lifespan is still <100 years even if you do all the right things (diet, exercise, sleep, don't smoke, etc).

We need a lot more progress.

Here are six paths I'm excited about:

1. The translation of discoveries in the last 30 years into actual medicines, such as senolytics, metformin, rapamycin, leveraging "young" blood and microbiota, etc
2. Cellular rejuvenation using Yamanaka factors
3. Cellular rejuvenation beyond Yamanaka factors, e.g. Sengstack et al (2022, BioRxiv)
4. A better understanding of why our cells and tissues age
5. Massive multi-modal 'omics and imaging data applied to aging
6. Reformulating aging biology wet lab experiments as computational/AI models, e.g. language models for cellular aging

These paths each have the potential to guide us to more healthy years.

One of the biggest challenges with slowing aging and improving health is that we don't closely measure how we age. Most people do annual blood tests, look for changes in how they look in the mirror, and wait until they get a specific disease. This is like taking basic measurements once a year or even less. It means our understanding of health and aging is very limited. We can't quickly try new things and see if they work. New therapies are beholden to the pace of slow, expensive clinical trials that take decades to bring new therapies to market. At this pace, we will have to get lucky to cure aging. In short, today's healthcare system leads to slow, incremental, analog improvements. 

Instead, we should track our complete health real-time. We should have real-time, continuous, zero-marginal-cost data on the health of every tissue and system, our rate of molecular damage throughout our bodies, and frequent predictions on our mounting health risks. It would provide tremendous understanding of aging and health. Then, we would apply modern ML tools to this data across lots of people with lots of health outcomes over time. These ML tools would be able to model our health, predict our health risks, and suggest interventions to improve our health. It's hard to overstate what this will bring.

Imagine a Chat-GPT for our own personal health. Let's call it Doc-GPT. Doc-GPT would be trained on data from hundreds of millions of people over many years. It will comb your data looking for risks and making suggestions. You will get reminders and suggestions frequently based on your risks and behavior. It will become your primary preventative physician.

I believe we are trending to this world. We will have automatic, continuous measurement of aging and health. Two big trends are making this possible: 1) the explosion of health data tracked on our phones and wearables, and 2) the exponential decrease in the cost of 'omics measurements. My iPhone and Apple Watch already have the data to make much better sense of my health than any doctor. They know my heart rate, gait, voice, daily activities, and much more. Our heart rate variability, voice, and gait each provide windows into our health and molecular damage. They have my photo library from the last 10 years, and faces provide a good window into our overall health [1]. They know how social and active we are, and how much we exercise. They know when people have heart attacks. They know when we go to the hospital. They can sense dementia. They probably know when we die. Apple has over 1 billion iPhone users and 100 million Watch users. They can see what increases or slows our aging. They can see what helps and hurts our health.

Our wearables and phones don't yet directly know the molecular damage in our cells and DNA. This is fundamental data to aging and health. Fortunately, 'omics measurement tools are riding an exponential curve trending towards essentially zero cost. ~20 years ago it cost billions of dollars to map the human genome. Today, we can do it for around $100. This is trending towards zero. As it does, we will know the status our DNA, epigenetics, RNA, and proteins much more frequently. It's hard to imagine having this data in real-time, but we are trending in that direction.

This health data from phones, wearables, and omics is absolutely massive. It's on the scale to build the Doc-GPT of the future. Apple, for example, may already be able to build a Doc-GPT with the data they have that is better than any doctor today. These data may replace much of what clinical trials do. These tools are coming, and they can't come soon enough. The race is on.

References:

[1] Xia X, Chen X, Wu G, Li F, Wang Y, Chen Y, Chen M, Wang X, Chen W, Xian B, Chen W, Cao Y, Xu C, Gong W, Chen G, Cai D, Wei W, Yan Y, Liu K, Qiao N, Zhao X, Jia J, Wang W, Kennedy BK, Zhang K, Cannistraci CV, Zhou Y, Han JJ. Three-dimensional facial-image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle. Nat Metab. 2020 Sep;2(9):946-957. doi: 10.1038/s42255-020-00270-x. Epub 2020 Sep 7. PMID: 32895578.   

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?

“Restoring order to the whole system is surely the eventual future of medicine. Unraveling the systems biology of aging is going to take incomprehensible amounts of data, enormous computing power, and smart computational biologists, working in tandem with those in the lab. Replacing numerical with narrative representation has revolutionized whole fields of science in the past, and the data and computation revolution in biology has only just begun. Once we can model our biology in detail, we’ll be able to reprogram it. Human beings will finally be negligibly senescent, biologically immortal, and ageless… It should be our collective mission.” - Andrew Steele, Ageless, 2020

One of the surprising things about aging science is how little we still know. We know far more than we did 40 years ago, thanks to a generation of pioneering investigators. But it seems that we have some puzzle pieces without the picture of how they fit together.

Another surprising thing is how little we know about our own bodies and health. We have high-level markers, such as blood panels, w physiological measures, etc. And we mostly rely on how we feel. If we feel good, we typically don't worry about our health. If we don't feel good, then something might be wrong. I generally feel great, young, energetic and healthy. Still, I know that damage from aging is accumulating throughout my 37 trillion cells and the tens of millions of biomolecules in each cell. Rust, damage and waste are taking root.

We don't yet know where this damage is happening. I can see the sun damage on my skin. I can feel my creaky ankles. But how is my heart aging? How is my brain aging? Where are little cancers forming? Which tissues are increasingly damaged that don't yet have noticeable dysfunction? It's hard to manage what you can't measure.

To Steele's quote, I believe it is critical to measure and model our biology. We need to know what's going on and intervene before it's too late. This will be a foundation for slowing and reversing aging. It's a massive challenge and will require new methods, data and computational techniques. It's where I expect to spend the next 20+ years of my career.

Are we getting better at treating age-related disease? I wouldn't know how to answer this. It has been discouraging to see the average lifespan fall in the last few years in the United States.

Encouragingly, a recent study argues that the risk of dementia has significantly declined in Americans from 2000 to 2016 [1]. From the study: "The age-adjusted prevalence of dementia decreased from 12.2% in 2000 (95% CI, 11.7 to 12.7%) to 8.5% in 2016 (7.9 to 9.1%) in the 65+ population, a statistically significant decline of 3.7 percentage points or 30.1%."

It is not clear what is driving this decrease. Reason's most recent Fight Aging newsletter speculates authors speculate it may have to do with better maintenance of blood pressure and the use of statins [2]. Neurodegenerative diseases like dementia are critical for lifespan extension. If we don't have a healthy brain, is life worth living? This study suggests we are making progress.

[1] Hudomiet, Hurd and Rohwedder. Trends in inequalities in the prevalence of dementia in the United States. PNAS 2022, 119 (46) e2212205119. https://doi.org/10.1073/pnas.2212205119  

[2] Reason. "The risk of suffering dementia is declining." Fight Aging. Accessed on Nov 19, 2022. URL: https://www.fightaging.org/archives/2022/11/the-risk-of-suffering-dementia-is-declining/

Many have proposed using the 9 hallmarks of aging as a roadmap for extending healthspan and lifespan. Encouragingly, this is actually happening.

As a reminder, these are the 9 hallmarks [1]:

1. Genomic instability
2. Telomere attrition
3. Epigenetic alterations
4. Loss of proteostasis
5. Deregulated nutrient sensing
6. Mitochondrial dysfunction
7. Cellular senescence
8. Stem cell exhaustion
9. Altered intercellular communication

The hallmarks are believed to be molecular drivers of aging. Thus, these are compelling targets. The hallmarks have held up pretty well since publication nearly 10 years ago now in 2013. You might argue that several of these are more important than others (e.g. loss of proteostasis (#4) seems especially damaging). You can also argue that these are not mutually exclusive or collectively exhaustive. They are deeply interwined. For example, cellular senescence (#7) leads to the senescence-associated secretory phenotype (SASP) which alters intercellular communication (#9). You might also argue that any of genomic instability (#1), epigenetic alterations (#3) or loss of proteostasis (#4) is an upstream cause of senescence. Still, slowing or reversing these hallmarks are likely to slow aging.

Researchers are learning how to reverse these hallmarks, both individually and in combination. For example, we have developed senolytics that remove senescent cells and show extended lifespan in mice [2], and human trials are underway [3]. One exciting recent paper combined partial reprogramming with senolytics in Drosophila (fruit flies) [4]. Each therapy on its own led to increased lifespan. Combining the two led to even larger increased in lifespan and the survival curve. The combined therapies led to improved stem cell proliferation, addressing hallmark 7, as well as reducing cellular senescence, addressing hallmark 7.

Researchers are also developing biomarkers for these hallmarks. A 2020 paper proposes a specific biomarker for each of the hallmarks of aging [5]. If we can measure the molecular basis of aging as it progresses, we can more directly develop therapies for model organisms and humans. These kinds of measurements could become successful consumer products for the longevity nerds of the world, similar to how biological age measurements have had direct-to-consumer commercial success.

One of the reasons I write this blog is to explore how to guide a long-term research program for aging research. Targeting the hallmarks of aging is one reasonable path 

References:

[1] Carlos López-Otín, Maria A. Blasco, Linda Partridge, Manuel Serrano, Guido Kroemer. The Hallmarks of Aging. Cell,Volume 153, Issue 6, 2013, pp. 1194-1217, https://doi.org/10.1016/j.cell.2013.05.039

[2] Xu, M., Pirtskhalava, T., Farr, J.N. et al. Senolytics improve physical function and increase lifespan in old age. Nat Med 24, 1246–1256 (2018). https://doi.org/10.1038/s41591-018-0092-9

[3] Search for "senolytics" at ClinicalTrials.gov. U.S. National Library of Medicine. Accessed Nov 13, 2022. URL: https://clinicaltrials.gov/ct2/results?cond=&term=senolytics&cntry=&state=&city=&dist=

[4] Kaur P, Otgonbaatar A, Ramamoorthy A, Chua EHZ, Harmston N, Gruber J, Tolwinski NS. Combining stem cell rejuvenation and senescence targeting to synergistically extend lifespan. Aging (Albany NY). 2022 Oct 25; 14:8270-8291. https://doi.org/10.18632/aging.204347

[5] Guerville, F., De Souto Barreto, P., Ader, I. et al. Revisiting the Hallmarks of Aging to Identify Markers of Biological Age. J Prev Alzheimers Dis 7, 56–64 (2020). https://doi.org/10.14283/jpad.2019.50

The life expectancy for a woman living in Japan is ~88 years, while the average American has life expectancy of ~79 [1]. Further, for a Japanese woman who does the known best practices for longevity and health (exercise, diet, no smoking, etc), life expectancy is likely easily over 90.

Thus, we have a science-translation gap of at least 11 years of lifespan for the average American. Healthier habits typically healthier aging, less disease and later onset of disease. It can also lead to lower rates of depression, greater wellbeing and greater productivity. Across 400 million Americans, that's 4.4 billion years of unnecessarily lost life. To me, this is a travesty and a huge opportunity for change. 

How do we close the gap? There are probably lots of ways to approach it. One idea is through a new company that does what Omada Health does for diabetes and hypertension, or what Octave Biosciences does for multiple sclerosis. As I understand it, these companies provide helpful interventions, often with a coach or advisor, to generate behavior change. For longevity and aging, simply increasing exercise and improving diet for Americans would be enough to close a lot of that gap. Healthcare payers, companies and our federal and state governments would benefit greatly if we did this. Maybe Omada will have more of a longevity focus over time? After all, alleviating diabetes and hypertension are closely related to slowing aging (diet, exercise, etc). Still, we would reach a much larger population if the focus was on extending lifespan vs. treating diabetes, pre-diabetes or hypertension. Both Omada and Octave are VC-backed. Might there be room for a similar new company focused on longevity?

Sources:

[1] Max Roser, Esteban Ortiz-Ospina and Hannah Ritchie (2013) - "Life Expectancy". Published online at OurWorldInData.org. Retrieved from: 'https://ourworldindata.org/life-expectancy' [Online Resource].

This figure (Buffenstein, 2008) shows maximum lifespan as a function of body mass of rodents. The naked mole rat maximum lifespan lies more than two standard deviations away from the trend line, according to the author. Naked mole rats also exhibit "negligible senescence" where the likelihood of death does not increase with age, while most other organisms including humans have exponentially increasing mortality rates.

I heard the author, Shelley Buffenstein, speak today at UW Nathan Shock Center 2022 Annual Geroscience Symposium. I almost skipped the talk (meh title). She was terrific. She is a world expert on the biology of naked mole rats. She recently published a book summarizing much of her research, The Extraordinary Biology of Naked Mole Rats. She spent the last 7 years at Calico (why did she leave?), and now is at University of Chicago. She has been publishing on the naked mole rat since at least 1991.

She shared this figure in her talk. It describes her view on how naked mole rats are able to achieve slower aging, including a more stable genome, transcriptome and proteome.

(Buffenstein, 2022)

Two parts were especially interesting. The first is the more stable cell cycle to avoid cancer. The second is the more stable proteome. The more stable proteome includes less protein synthesis, higher translational accuracy, upregulated HSP70 and HSP27, increased autophagy, increased proteosome activity and upregulated NRF2 and antioxidants. She shared data that the prevalence of misfolded proteins is much lower than in mice generally and in response to stress. I imagine the proteome of a naked mole rat to a city with new buildings, no traffic and no trash on the street, while the proteome of a mouse (and human) a crowded, messy city in disrepair.

How can humans get a more orderly proteome? We hear that diet, exercise and other aging-related compounds can help with things like autophagy. But how do we supercharge these pathways that seem to slow disorder and disease?

Evolution is smarter than we are. Evolution has figured out longevity pathways in naked mole rats, as well as other organisms. For example, Shelley described that when bears hibernate and don't move for six months, they don't lose bone mass, while humans do. As another example, elephants have double copies of the p53 gene, which reduces cancer risk.

How can we bring evolution's brilliant longevity discoveries into humans?

References:

Buffenstein, R. Negligible senescence in the longest living rodent, the naked mole-rat: insights from a successfully aging species. J Comp Physiol B 178, 439–445 (2008). https://doi.org/10.1007/s00360-007-0237-5

Aging as a complex system that loses homeostasis

Is aging the result of a very complex machine falling out of homeostasis? This is a compelling high-level paradigm for aging.

Why do different organisms have different rates of aging and different lifespans? Evolution shaped each species to survive. The fly’s lifespan worked for it given all its other constraints. Human lifespans are relatively long and they have worked for us so far. Certain other organisms like Greenland sharks and bristlecone pines have even longer lifespans. The variation is orders of magnitude.

It is very likely that we can reprogram flies to live 100x longer. I also think it is very likely that we can reprogram people to live 10x longer. It is at least conceivable. Rejuvenation of the critical parts of our complex bodies should in theory fight the loss of homeostasis. Epigenetic reprogramming is starting to provide a proof of concept.

Given the complexity of all living things, and especially humans, I think we need to first prove 10x lifespan extension in smaller organisms. Maybe flies, maybe worms, maybe yeast. If we can’t do it for those organisms, it seems very difficult to do it for humans. Maybe some brilliant researchers will bypass this step and solve it for humans? I'm not sure how they will.

We have the opportunity to create accurate models of aging. Right now aging is too complex and we only have overly simplistic models. With the twin innovations of big data / ‘omics and deep learning, we are poised for data-driven discovery of the complex, high-dimensional, combinatorial interactions driving aging. First things first: let’s get this working in simple organisms.

A recent paper tied the Black Plague in the 1400s rapid evolution in the human immune system. I believe we are again on the cusp of rapid evolution. We will begin to edit our genes and rejuvenate our bodies. Buckle up.

Here are some of the very biggest questions in aging science as I see it:

1. What is aging? How do you measure aging on shorter timescales than death?
2. What drives aging? How? Is the epigenome the primary driver?
3. What slows aging? How? Which of the various possible interventions work? What combinations of these interventions is optimal?
4. What reverses aging? How? Does cellular reprogramming reverse aging?
5. What should humans do to live longer and healthier? How personalized do interventions need to be? How do I know if I'm doing the right things for my aging?
6. How can we develop a simpler model of aging to answer the above questions? While the particulars are different, almost every organism ages. Mammals are so large and complex, and we still don't understand many fundamentals. Could we start with C. elegans with ~1,000 somatic cells vs. ~37 trillion cells for humans?

What data could illuminate our understanding of these fundamental questions?

Here is a data pipeline that would be very interesting:

• Start with a tiny, well-understood organism like C. elegans.
• Do whole organism epigenomics, spatial transcriptomics, genomics and imaging across the full lifespan. Maybe 10-100 unique time points so you can see changes over time? This is a tremendous amount of data.
• Identify spatio-temporal aging patterns.
• Create a "biological clock" based on all this data.
• Pick candidates for "drivers" of aging, e.g. epigenomic remodeling, and then intervene/perturb to return towards "youthful" state.
• Elucidate drivers of aging and methods for slowing and reversing aging.
• Once this works, expand to larger and larger organisms.

I'm sure others have considered this kind of approach. Why aren't we doing it today?

These are some of the hottest topics right now. I've learned in that last week that each of them has armies of brilliant people working on them. 

1. Protein language models. These have the potential for algorithms to learn fundamental dynamics of protein folding and interaction. This could get us one step closer to modeling human biology. Once we can model human biology at the molecular and cellular level, our ability to model disease and interventions will be far better than today. Brilliant AI researchers at Facebook and DeepMind are working on this. Brilliant researchers are dozens or more labs around the world on working this.

2. Epigenetic reprogramming. Partial epigenetic reprogramming has been shown to rejuvenate cells, tissues and even whole mice. There are concerning off-target effects (cancer is a particular concern). The potential for slowing and reversing aging is easy to see. It could be the great human breakthrough of the 21st century. There are dozens if not hundreds or thousands of labs around the world working on this. Startups like Altos Labs ($3 billion of initial funding!), NewLimit and many others are on the case.

3. Aging clocks. How do you measure aging, beyond waiting to see if someone dies or not? This is an important questions. You can't manage what you can't measure. There are compelling epigenetic clocks, plus a whole host of new biomarker clocks, transcriptomic clocks, proteomic clocks, etc. There are many brilliant people in academic and industry labs working on this.

Does this mean I and others in the field should go work on these topics?

It depends. I think it's great that we have so much energy on these 3 important topics. It increases the chance that we get answers and progress on them faster. And there are people who thrive in an all-out race to see who can be first. At the same time, science (and investing and most human endeavors) are prone to herd mentality. We need smart people exploring the dusty corners of biology and aging. Unpopular ideas are often ultimately right. Some people do their best work with the peace and freedom of unpopular fields

Stanley B. Prusiner of UCSF writes about this in his 2014 book, Madness and Memory: The Discovery of Prions--A New Biological Principle of Disease. The research community met his early theories with disdain. Yet, he was ultimately right in his views on prions and brain disease.

My view is that we should follow closely the latest breakthroughs and trends in our fields. There are new ideas that can be transformative. It can be easier to get funding and energy when you're on a popular topic. At the same time, scientific researchers should follow their best judgment, even if it leads to an unpopular place.

I listened to a number of talks at the Mechanisms of Aging conference at Cold Spring Harbor Laboratory (CSHL) this week [1].

There were four back-to-back talks today that were especially promising for epigenetic reprogramming:

1. PRC2 clock—A universal epigenetic biomarker of aging and rejuvenation by Vittorio Sebastiano (Stanford University). Vittorio shared data supporting a new epigenetic biomarker of age, which is the DNA methylation of PRC2 in lowly methylated regions [2]. They found that this single measure could account for 90% of the DNA methylation age from other clocks pulling on many regions. The benefit of this clock is that is interpretable (measured directly) and without model bias (no training). It also suggests alignment with an underlying epigenetic mechanism, as PRC2 catalyzes histone methylation. Interestingly, Vittorio found in mice that rapomycin did not lower the PRC2 clock "age", while caloric restriction did.

2. Reprogramming to recover youthful epigenetic information and restore vision in age-related macular degeneration (sic) by Yuancheng Lu (Sinclair Lab, Ksander Lab, Margerete Karg; Harvard University). Yuancheng was the first author on the incredible Nature 2020 paper that showed youthful DNA methylation patterns, axon regeneration and vision restoration in mice with optic nerve crush and mice with a model of glaucoma [3]. This talk reiterated that data and then shared new data on similar results in a mouse model of macular degeneration. OSK were again the reprogramming factors used. No evidence of terratoma/cancer were found after a period of observation, though this bears more study. Most encouraging is that epigenetic reprogramming 1) works in a new use case (macular degeneration) and 2) did not lead to cancer. A paper is forthcoming.

3. Epigenetic reprogramming reverses neuronal aging and improves cognitive performance by Xiao Tian (Sinclair Lab, Harvard University). Xiao built on Yuancheng's work with a focus on the brain. There have been concerns that the blood-brain-barrier would make it difficult to deliver epigenetic reprogramming to the brain. Xiao & team created a new AAV to address this. They showed powerful benefits in the brain for memory and vs. old mice and mice with a model of Alzheimer's. Once again, this provides a powerful new use case. A paper is forthcoming.

4. Transcriptomic reprogramming screen using functional RNA clock for cellular rejuvenation by Alex Plesa (Church Lab, Harvard University). Alex pointed out that reprogramming been focused on the OKSM Yamanaka factors for over a decade now. They wondered if there might be other factors that work as well, that might be safer or have other complementary effects. Using a functional RNA clock (vs. a DNA methylation clock), they identified several candidates. SRSF1, a protein splicer and regulator of transcription, was the most promising in their data. When will we see SRSF1 tested in experimental models of aging? A paper is forthcoming.

Together, these build support for the promise of epigenetic reprogramming in treating aging. One of the most important things for long-term aging science is to identify the right paradigm for the core drivers of aging. Epigenetic reprogramming seems to address aging by rewiring gene expression to a more youthful state. This seems to align with a paradigm where epigenetic unwinding leads to loss of protein homeostasis, which then leads to cellular and tissue dysfunction. The talks above shared new data points in new situations (macular degeneration, Alzheimer's, etc), which shows that reprogramming might be fundamental across cells and tissues. It also points out that there may be other or complementary reprogramming factors, like SRSF1, that could overcome obstacles with OSK (or OSKM).

What comes next? We need to see if epigenetic reprogramming can work in vivo across tissues and lead to better health and longer life. This will require continued work, like Xiao's, to find ways to get the factors safely to their target. It would be especially convincing to see this across multiple organisms (worms, flies, mice, rats, primates?), to prove it is a conserved feature. How long will it be before we have a mouse living for 5+ years with epigenetic reprogramming?

References:

[1] Full list of abstracts for the 2022 Mechanisms of Aging conference at CSHL: https://meetings.cshl.edu/abstracts.aspx?meet=AGING&year=22

[2] Moqri et al. PRC2 clock: a universal epigenetic biomarker of aging and rejuvenation. bioRxiv (2022). doi: https://doi.org/10.1101/2022.06.03.494609

[3] Lu, Y., Brommer, B., Tian, X. et al. Reprogramming to recover youthful epigenetic information and restore vision. Nature 588, 124–129 (2020). https://doi.org/10.1038/s41586-020-2975-4

Let's start with the big goal that humanity should work to achieve significant lifespan and healthspan extension by 2063. That gives us 41 years, with no time to spare. How can we make sure we're on the right track? How do we know we're working on the right things?

Here are some questions that could help guide a long-term aging research program:

1. What are examples of research programs that made major progress on an intractable disease, e.g. multiple sclerosis? Why did it work? What hasn't worked? What can we recreate for aging science?

2. What does a multi-decade research program look like to significantly extend human lifespan and healthspan? What are some examples of this done well in aging? Done poorly?

3. What is the right "paradigm" for aging science? Thomas Kuhn's The Structure of Scientific Revolutions argues that identifying the right "paradigm" or high-level lens for a problem is critical. Until you get the right high-level paradigm for a problem, researchers can be focused on the wrong things. For aging, there is not consensus on what causes it. We have 9 "hallmarks" of aging, but what are the drivers?

One tantalizing theory is the "information theory of aging." It goes something like this:

1. DNA damage => epigenetic unwinding
2. Epigenetic unwinding => loss of transcriptional control
3. Loss of transcriptional control => protein noise
4. Protein noise => cellular dysfunction
5. Cellular dysfunction => tissue dysfunction
6. Tissue dysfunction => increased risk of disease and death

Is this right, or at least close enough? What would be true if this theory were right? For example, do organisms with slower aging have slower DNA damage and less epigenetic unwinding? Does each item in the chain hold, e.g. if you reduce DNA damage, do you get less epigenetic unwinding, etc.

If we can settle on a good paradigm, we could focus our efforts.

4. How would we know we're on track? Can you even predict what the benchmarks might be? Science can feel like you're wandering in the woods, and it's hard to know if you're making real progress or not.

5. Pre-mortem: How might we fail? In other words, what are things that could have gone wrong such that we don't make major progress in aging science over the next 40 years? One interesting example of this is the case of Alzheimer's disease research. Karl Herrup's How Not To Study a Disease: The Story of Alzheimer's argues that an excessive focus on the amyloid beta hypothesis came at the expense of other inquiry and led to a herd mentality among funders and researchers. Are there other similar examples?

6. How do we answer questions 1-5? I expect it will take a while to form a good perspective. I'll start by reading a lot of scientific histories. There are good books written about many of the major scientific discoveries (E.g. James Watson's The Double Helix). I'm working down a list, but please share any recommendations you might have. Reading the seminal papers on each field (e.g. aging, Alzheimer's, Parkinsons, spatial transcriptomics, deep learning, etc) help give clues to how intellectual progress happens. I'm also asking these questions of people who might know. UCSF and the SF Bay Area is a great place for this, and I've learned a lot from the coffee chats and lunches with top researchers.

Maybe science doesn't proceed in this systematic of a manner? Still, if we answer these questions I believe we will find better approaches to aging science.

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.

The idea of extending lifespan is a controversial topic. Some believe it is unnatural. Others question if living longer while sick and unhealthy is worth it. Some wonder if the world can sustain more people. These are all important questions. Below I summarize the cases I've heard against and for this research. Then, I'll share my perspective (which you may be able to guess).

The case against research to extend human longevity

1. Isn’t this unnatural?

2. Is it worth living longer if you’re just sick and unhealthy? I don’t want to be like grandparents at the end of their life.

3. Can the world sustain more people? Are we already beyond the world’s carrying capacity?

4. Will evil dictators remain in power for too long?

5. Will society become stifled if the old don’t make room for the young?

6. What if I get bored of my life? Of my significant other?

7. Will only a select few be able to afford the longevity treatments?

8. Is it even possible? Human aging is incredibly complex. 

The case for research to extend human longevity

1. Keep family and friends healthy and alive!

2. Aging and age-related disease is the leading killer globally.

3. We are already on board with curing age-related diseases like cancer, dementia and frailty, which are extensions of aging.

4. We have already more than doubled lifespan in the last 100 years.

5. We lose so much wisdom, experience and human connection when people die.

6. Longevity interventions in model organisms and humans show increased healthspan as well as increased lifespan.

7. The healthcare savings would be enormous, given most money is spent in the last 6 months of life.

8. If someone doesn’t love life, they don’t have to take the longevity treatments.

9. Healthcare innovations typically get shared broadly over time. For example, global average life expectancy is around 70 years, including lower-income countries, vs. 75 years in the United States.

My perspective: I want my family and friends to stay healthy and alive for as long as possible. I love life and I want to keep living. I believe in technology development and global progress over the last 150 years has been on average very good for humanity in both rich and lower-income countries. I believe this will continue if we keep making amazing technologies and innovations like lifespan extension.

I believe humans can have 200+ year lifespans/healthspans. One big reason is that other species already do it.

For example, ocean quahogs live for 200+ years. Greenland sharks live for 250-500 years. Redwood trees can live for over 3,000 years. Bristlecone pines can live for 5,000 years.

Some species of rockfish live for 11 years on average. Some live for 200+ years. Why?

In fact, a study from Sudmant Lab (Science, 2021) studied 88 species of rockfish with various lifespans to identify genetic drivers of lifespan. They found that immunity and DNA repair pathways were associated with longer life. They posited that inflammation may have a major role in aging. 

Evolution has figured out long lifespan already. Let's see what we can learn.

There are a few questions I'd like to explore:

1. What is the full list of organisms that live for 100s of years?
2. How they do they do it?
3. Why did they evolve that way?
4. What can humans do to mimic this? e.g. gene editing for better DNA repair? 

References:

Kolora, et al. Origins and evolution of extreme life span in Pacific Ocean rockfishes. Science, 11 Nov 2021, Vol 374, Issue 6569, pp. 842-847. DOI: 10.1126/science.abg5332

The ultimate measure of Longevity Technology is how long people live.

One of the great successes in human history is the relatively linear growth of global life expectancy from 30 years pre-1870 to 73 years in 2019[1].

Globally, we increased life expectancy by 143% in 149 years. This is roughly an additional year of average lifespan every 3.5 years.

But that's just a global average. If you focus on specific populations, longevity technology does better. Take Japanese women, for example, with an expected life expectancy of ~89 years. That's great. Further, the top decile of Japanese women are likely expected to live close to 100 years.

Thus, our global measure of Longevity Technology is 73 years of global average life expectancy. Plus, we have pockets of early adopting populations with ~100 year median lifespans.

Are you or I likely to live to 100 years? We don't have good data or models on this (this is a gap). Lifespan is a backward-looking metric because it can take decades or even a century to know what happened.

There are relatively proven Longevity Technologies, and there are ones that are likely coming soon.

Relatively proven technologies:

• Eat a mostly plants, fruits, nuts and legumes vegan diet and avoid added sugar. There was a recent meta study suggesting this would give ~10-13 years of additional expected lifespan vs. a typical (not even a terrible) Western diet. Simply doing a mediterranean diet probably gets you most of those years. 
• Exercise 7+ hours per week. Include some resistance training to reduce muscle and bone loss. 
• Sleep 7-8 hours per night.
• Enjoy life, enjoy work, enjoy family, and have a solid social life.
• Don't smoke or use opiates. Occasional or no alcohol.
• Avoid accidents.

This on its own probably gives an expected lifespan of around 90-100. I try to do all of these, though don't always succeed. They have become part of my routine, and I don't feel like I'm making any compromises on my happiness. That said, we don't really know what doing all the "right" things get us. Once again, we need better data on this.

Technologies that may be coming soon:

• Gene editing to enhance longevity genes and remove deleterious genes. For example, a woman in New Zealand recently had a deleterious cholesterol gene edited out. There is significant, untapped opportunity here. What genes do Japanese women have that might be helpful? What genes do centenarians have that might be helpful?
• New drugs, like Rapamycin or Metformin, that promise
• New treatments for diseases like cancer, stroke, heart attack, dementia, etc.
• New centenarian treatments. The number of centenarians is growing rapidly, and we will likely pass 1 million centenarians globally shortly. This will give us the opportunity to test many new treatments for this age group to help them live longer and healthier. 
  

It's not hard to imagine in 10 or 20 years the early adopters of Longevity Technology will have an expected lifespan of 120 years. Though, it might take a few decades to really know.

References:

[1] Max Roser, Esteban Ortiz-Ospina and Hannah Ritchie. "Life Expectancy." Our World in Data. First published in 2013; last revised in October 2019. Accessed July 22, 2022. https://ourworldindata.org/life-expectancy

We still know very little about human aging.

For example, how is my liver aging? How is my brain aging? I have no idea.

I am drawn to Prof. Michael Snyder's work on extreme measurement of his body. He has collected petabytes of data about his body physiology and genetics. This kind of data will help us measure aging-related interventions with more detail and personalization.

Prof. Snyder is also involved in the ENCODE project, which has the goal of "developing a comprehensive map of functional elements in the human genome" (Snyder et al, Nature, 2020). Many other large-scale (and small-scale) projects like these are in flight. Taken together, all this new data will give us a better systems-level understanding of biology and aging.

If we can know which genes are likely to contribute to aging, personalized for each individual's unique physiology and genes, then we can change our genes to extend our lives. In fact, we may already be doing this. Recently a volunteer in New Zealand had a cholesterol gene edited with CRISPR to a lower-risk version ("Edits to a cholesterol gene could stop the biggest killer on earth", MIT Technology Review, July 2022). This could be life-extending gene editing, here and now.

We are in the early days of systems biology. We are in the very early days of curing aging. Exponential curves can move quickly. We might be surprised what we can do in 20 years.

Are we making progress in life extension? Will it be in my lifetime?

Here's a signal that we're making real progress in life extension: when we have interventions that allow mice, rats, dogs and monkeys to live much longer and healthier than they currently do.

We're not there yet.

We've seen some incremental evidence of life extension in mice. Still, we can't get a mouse to live reliably to 5 years old and beyond. We can't get a rat to live much beyond 6 or 7, though maybe Harold Katcher's blood factors will change that?

Mice and rats are interesting, especially given the naked mole rat. Naked mole rats can live for 30+ years. They age much more slowly. They have better DNA repair. They get less cancer. They're similar to rats and mice (though more similar to guinea pigs, which live 5-7 years).

One interesting research path would be to make mice and rats (or guinea pigs) more like naked mole rats. Maybe gene editing or drugs to stimulate the same DNA repair pathways?

We also haven't seen much life extension yet in dogs and monkeys. The Dog Aging Project may change this soon. This study is giving rapamycin to older companion dogs.

Epigenetic reprogramming is promising and high-visibility. We'll know it's especially promising when we see mice, rats, dogs and monkeys living longer and healthier because of it. Same with the factors in young blood.

Put another way, if we can't reliably increase lifespan and healthspan in mice, rats, dog and monkeys, we probably not that close to achieving it in humans.

One of the most important questions within aging science is identifying good biomarkers of aging.

We need biomarkers that are predictive of aging rate and mortality. We have lots of biomarkers that are associated with aging rate and mortality, like cholesterol readings and epigenetic clocks. But we don't know how predictive they are.

We need these biomarkers to be more frequently measured than a full human lifespan. That way we can test interventions and see how they work. The more frequently these biomarkers can be taken (and still be predictive), the more we can make lots of personalized interventions (e.g. lifestyle, drugs, epigenetic reprogramming, gene editing).

Once we have these biomarkers, I could imagine a device like an Apple Watch giving us personalized recommendations based on population and personal data on how to slow our aging. Apple Watch captures large amounts of physiological and lifestyle data. They are increasing their data-capturing capabilities every year.

Working on biomarkers of aging is likely a very good use of time.