Drugs That Could Slow Aging May Hold Promise for Protecting the Elderly from COVID-19
Although recent data has shown the coronavirus poses a greater risk to young people than previously understood, the ensuing COVID-19 disease is clearly far more dangerous for older people than it is for the young.
If we want to lower the COVID-19 fatality rate, we must also make fortifying our most vulnerable hosts a central part of our approach.
While our older adults have accrued tremendous knowledge, wisdom, and perspective over the years, their bodies have over time become less able to fight off viruses and other insults. The shorthand name for this increased susceptibility is aging.
We may have different names for the diseases which disproportionately kill us -- cancer, heart disease, and dementia among them – but what is really killing us is age. The older we are, the greater the chance we'll die from one or another of these afflictions. Eliminate any one completely - including cancer - and we won't on average live that much longer. But if we slow aging on a cellular level, we can counter all of these diseases at once, including COVID-19.
Every army needs both offensive and defensive capabilities. In our war against COVID-19, our offense strategy is to fight the virus directly. But strengthening our defense requires making us all more resistant to its danger. That's why everyone needs to be eating well, exercising, and remaining socially connected. But if we want to lower the COVID-19 fatality rate, we must also make fortifying our most vulnerable hosts a central part of our approach. That's where our new fight against this disease and the emerging science of aging intersect.
Once the domain of charlatans and delusionists, the millennia-old fantasy of extending our healthy lifespans has over the past century become real. But while the big jump in longevity around the world over the past hundred years or so is mostly attributable to advances in sanitation, nutrition, basic healthcare, and worker safety, advances over the next hundred will come from our increasing ability to hack the biology of aging itself.
A few decades ago, scientists began recognizing that some laboratory animals on calorie-restricted diets tended to live healthier, longer lives. Through careful experiments derived from these types of insights, scientists began identifying specific genetic, epigenetic, and metabolic pathways that influence how we age. A range of studies have recently suggested that systemic knobs might metaphorically be turned to slow the cellular aging process, making us better able to fight off diseases and viral attacks.
Among the most promising of these systemic interventions is a drug called metformin, which targets many of the hallmarks of aging and extends health span and lifespan in animals. Metformin has been around since the Middle Ages and has been used in Europe for over 60 years to treat diabetes. This five-cent pill became the most prescribed drug in the world after being approved by the FDA in 1994.
With so many people taking it, ever larger studies began suggesting metformin's positive potential effects preventing diabetes, cardiovascular diseases, cancer, and dementia. In fact, elderly people on metformin for their diabetes have around a 20 percent lower mortality than age-matched subjects without diabetes. Results like these led scientists to hypothesize that metformin wasn't just impacting a few individual diseases but instead having a systemic impact on entire organisms.
Another class of drug that seems to slow the systemic process of aging in animal models and very preliminary human trials inhibits a nutrient-sensing cellular protein called mTOR. A new category of drugs called rapalogues has been shown to extend healthspan and lifespan in every type of non-human animal so far tested. Two recent human studies indicated that rapalogues increased resistance to the flu and decreased the severity of respiratory tract infections in older adults.
If COVID-19 is primarily a severe disease of aging, then countering aging should logically go a long way in countering the disease.
These promising early indications have inspired a recently launched long-term study exploring how metformin and rapalogues might delay the onset of multiple, age-related diseases and slow the biological process of aging in humans. Under normal circumstances, studies like this seeking to crack the biological code of aging would continue to proceed slowly and carefully over years, moving from animal experiments to cautious series of human trials. But with deaths rising by the day, particularly of older people, these are not times for half measures. Wartimes have always demanded new ways of doing important things at warp speeds.
If COVID-19 is primarily a severe disease of aging, then countering aging should logically go a long way in countering the disease. We need to find out. Fast.
Although it would be a mistake for older people to just begin taking drugs like these without any indication, pushing to massively speed up our process for assessing whether these types of interventions can help protect older people is suddenly critical.
To do this, we need U.S. government agencies like the Department of Health and Human Services' Biomedical Advanced Research and Development Authority (BARDA) to step up. BARDA currently only funds COVID-19 clinical trials of drugs that can be dosed once and provide 60 days of protection. Metformin and rapalogues are not considered for BARDA funding because they are dosed once daily. This makes no sense because a drug that provides 60 days of protection from the coronavirus after a single dose does not yet exist, while metformin and rapalogues have already passed extensive safety tests. Instead, BARDA should consider speeding up trials with currently available drugs that could help at least some of the elderly populations at risk.
Although the U.S. Food and Drug Administration and Centers for Disease Control are ramping up their approval processes and even then needs to prioritize efforts, they too must find a better balance between appropriate regulatory caution and the dire necessities of our current moment. Drugs like metformin and rapalogues that have shown preliminary efficacy ought to be fast-tracked for careful consideration.
One day we will develop a COVID-19 vaccine to help everyone. But that could be at least a year from now, if not more. Until we get there and even after we do, speeding up our process of fortifying our older populations mush be a central component of our wartime strategy.
And when the war is won and life goes back to a more normal state, we'll get the added side benefit of a few more months and ultimately years with our parents and grandparents.
One day in recent past, scientists at Columbia University’s Creative Machines Lab set up a robotic arm inside a circle of five streaming video cameras and let the robot watch itself move, turn and twist. For about three hours the robot did exactly that—it looked at itself this way and that, like toddlers exploring themselves in a room full of mirrors. By the time the robot stopped, its internal neural network finished learning the relationship between the robot’s motor actions and the volume it occupied in its environment. In other words, the robot built a spatial self-awareness, just like humans do. “We trained its deep neural network to understand how it moved in space,” says Boyuan Chen, one of the scientists who worked on it.
For decades robots have been doing helpful tasks that are too hard, too dangerous, or physically impossible for humans to carry out themselves. Robots are ultimately superior to humans in complex calculations, following rules to a tee and repeating the same steps perfectly. But even the biggest successes for human-robot collaborations—those in manufacturing and automotive industries—still require separating the two for safety reasons. Hardwired for a limited set of tasks, industrial robots don't have the intelligence to know where their robo-parts are in space, how fast they’re moving and when they can endanger a human.
Over the past decade or so, humans have begun to expect more from robots. Engineers have been building smarter versions that can avoid obstacles, follow voice commands, respond to human speech and make simple decisions. Some of them proved invaluable in many natural and man-made disasters like earthquakes, forest fires, nuclear accidents and chemical spills. These disaster recovery robots helped clean up dangerous chemicals, looked for survivors in crumbled buildings, and ventured into radioactive areas to assess damage.
Now roboticists are going a step further, training their creations to do even better: understand their own image in space and interact with humans like humans do. Today, there are already robot-teachers like KeeKo, robot-pets like Moffin, robot-babysitters like iPal, and robotic companions for the elderly like Pepper.
But even these reasonably intelligent creations still have huge limitations, some scientists think. “There are niche applications for the current generations of robots,” says professor Anthony Zador at Cold Spring Harbor Laboratory—but they are not “generalists” who can do varied tasks all on their own, as they mostly lack the abilities to improvise, make decisions based on a multitude of facts or emotions, and adjust to rapidly changing circumstances. “We don’t have general purpose robots that can interact with the world. We’re ages away from that.”
Robotic spatial self-awareness – the achievement by the team at Columbia – is an important step toward creating more intelligent machines. Hod Lipson, professor of mechanical engineering who runs the Columbia lab, says that future robots will need this ability to assist humans better. Knowing how you look and where in space your parts are, decreases the need for human oversight. It also helps the robot to detect and compensate for damage and keep up with its own wear-and-tear. And it allows robots to realize when something is wrong with them or their parts. “We want our robots to learn and continue to grow their minds and bodies on their own,” Chen says. That’s what Zador wants too—and on a much grander level. “I want a robot who can drive my car, take my dog for a walk and have a conversation with me.”
Columbia scientists have trained a robot to become aware of its own "body," so it can map the right path to touch a ball without running into an obstacle, in this case a square.
Jane Nisselson and Yinuo Qin/ Columbia Engineering
Today’s technological advances are making some of these leaps of progress possible. One of them is the so-called Deep Learning—a method that trains artificial intelligence systems to learn and use information similar to how humans do it. Described as a machine learning method based on neural network architectures with multiple layers of processing units, Deep Learning has been used to successfully teach machines to recognize images, understand speech and even write text.
Trained by Google, one of these language machine learning geniuses, BERT, can finish sentences. Another one called GPT3, designed by San Francisco-based company OpenAI, can write little stories. Yet, both of them still make funny mistakes in their linguistic exercises that even a child wouldn’t. According to a paper published by Stanford’s Center for Research on Foundational Models, BERT seems to not understand the word “not.” When asked to fill in the word after “A robin is a __” it correctly answers “bird.” But try inserting the word “not” into that sentence (“A robin is not a __”) and BERT still completes it the same way. Similarly, in one of its stories, GPT3 wrote that if you mix a spoonful of grape juice into your cranberry juice and drink the concoction, you die. It seems that robots, and artificial intelligence systems in general, are still missing some rudimentary facts of life that humans and animals grasp naturally and effortlessly.
How does one give robots a genome? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create.
It's not exactly the robots’ fault. Compared to humans, and all other organisms that have been around for thousands or millions of years, robots are very new. They are missing out on eons of evolutionary data-building. Animals and humans are born with the ability to do certain things because they are pre-wired in them. Flies know how to fly, fish knows how to swim, cats know how to meow, and babies know how to cry. Yet, flies don’t really learn to fly, fish doesn’t learn to swim, cats don’t learn to meow, and babies don’t learn to cry—they are born able to execute such behaviors because they’re preprogrammed to do so. All that happens thanks to the millions of years of evolutions wired into their respective genomes, which give rise to the brain’s neural networks responsible for these behaviors. Robots are the newbies, missing out on that trove of information, Zador argues.
A neuroscience professor who studies how brain circuitry generates various behaviors, Zador has a different approach to developing the robotic mind. Until their creators figure out a way to imbue the bots with that information, robots will remain quite limited in their abilities. Each model will only be able to do certain things it was programmed to do, but it will never go above and beyond its original code. So Zador argues that we have to start giving robots a genome.
How does one do that? Zador has an idea. We can’t really equip machines with real biological nucleotide-based genes, but we can mimic the neuronal blueprint those genes create. Genomes lay out rules for brain development. Specifically, the genome encodes blueprints for wiring up our nervous system—the details of which neurons are connected, the strength of those connections and other specs that will later hold the information learned throughout life. “Our genomes serve as blueprints for building our nervous system and these blueprints give rise to a human brain, which contains about 100 billion neurons,” Zador says.
If you think what a genome is, he explains, it is essentially a very compact and compressed form of information storage. Conceptually, genomes are similar to CliffsNotes and other study guides. When students read these short summaries, they know about what happened in a book, without actually reading that book. And that’s how we should be designing the next generation of robots if we ever want them to act like humans, Zador says. “We should give them a set of behavioral CliffsNotes, which they can then unwrap into brain-like structures.” Robots that have such brain-like structures will acquire a set of basic rules to generate basic behaviors and use them to learn more complex ones.
Currently Zador is in the process of developing algorithms that function like simple rules that generate such behaviors. “My algorithms would write these CliffsNotes, outlining how to solve a particular problem,” he explains. “And then, the neural networks will use these CliffsNotes to figure out which ones are useful and use them in their behaviors.” That’s how all living beings operate. They use the pre-programmed info from their genetics to adapt to their changing environments and learn what’s necessary to survive and thrive in these settings.
For example, a robot’s neural network could draw from CliffsNotes with “genetic” instructions for how to be aware of its own body or learn to adjust its movements. And other, different sets of CliffsNotes may imbue it with the basics of physical safety or the fundamentals of speech.
At the moment, Zador is working on algorithms that are trying to mimic neuronal blueprints for very simple organisms—such as earthworms, which have only 302 neurons and about 7000 synapses compared to the millions we have. That’s how evolution worked, too—expanding the brains from simple creatures to more complex to the Homo Sapiens. But if it took millions of years to arrive at modern humans, how long would it take scientists to forge a robot with human intelligence? That’s a billion-dollar question. Yet, Zador is optimistic. “My hypotheses is that if you can build simple organisms that can interact with the world, then the higher level functions will not be nearly as challenging as they currently are.”
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.
Podcast: Wellness chatbots and meditation pods with Deepak Chopra
Over the last few decades, perhaps no one has impacted healthy lifestyles more than Deepak Chopra. While several of his theories and recommendations have been criticized by prominent members of the scientific community, he has helped bring meditation, yoga and other practices for well-being into the mainstream in ways that benefit the health of vast numbers of people every day. His work has led many to accept new ways of thinking about alternative medicine, the power of mind over body, and the malleability of the aging process.
His impact is such that it's been observed our culture no longer recognizes him as a human being but as a pervasive symbol of new-agey personal health and spiritual growth. Last week, I had a chance to confirm that Chopra is, in fact, a human being – and deserving of his icon status – when I talked with him for the Leaps.org podcast. He relayed ideas that were wise and ancient, yet highly relevant to our world today, with the fluidity and ease of someone discussing the weather. Showing no signs of slowing down at age 76, he described his prolific work, including the publication of two books in the past year and a range of technologies he’s developing, including a meditation app, meditation pods for the workplace, and a chatbot for mental health called Piwi.
Take a listen and get inspired to do some meditation and deep thinking on the future of health. As Chopra told me, “If you don’t have time to meditate once per day, you probably need to meditate twice per day.”
Highlights:
2:10: Chopra talks about meditation broadly and meditation pods, including the ones made by OpenSeed for meditation in the workplace.
6:10: The drawbacks of quick fixes like drugs for mental health.
10:30: The benefits of group meditation versus individual meditation.
14:35: What is a "metahuman" and how to become one.
19:40: The difference between the conditioned mind and the mind that's infinitely creative.
22:48: How Chopra's views of free will differ from the views of many neuroscientists.
28:04: Thinking Fast and Slow, and the role of intuition.
31:20: Athletic and creative geniuses.
32:43: The nature of fundamental truth.
34:00: Meditation for kids.
37:12: Never alone.Love and how AI chatbots can support mental health.
42:30: Extending lifespan, gene editing and lifestyle.
46:05: Chopra's mentor in living a long good life (and my mentor).
47:45: The power of yoga.
Links:
- OpenSeed meditation pods for people to meditate at work (Chopra is an advisor to OpenSeed).
- Chopra's book from 2021, Metahuman: Unleash Your Infinite Potential
- Chopra's book from 2022, Abundance: The Inner Path to Wealth
- NeverAlone.Love, Chopra's collaboration of businesses, policy makers, mental health professionals and others to raise awareness about mental health, advance scientific research and "create a global technology platform to democratize access to resources."
- The Piwi chatbot for mental health
- The Chopra Meditation & Well-Being App for people of all ages
- Only 1.6 percent of U.S. children meditate, according to the National Center for Complementary and Integrative Health