Technology is Redefining the Age of 'Older Mothers'
In October 2021, a woman from Gujarat, India, stunned the world when it was revealed she had her first child through in vitro fertilization (IVF) at age 70. She had actually been preceded by a compatriot of hers who, two years before, gave birth to twins at the age of 73, again with the help of IVF treatment. The oldest known mother to conceive naturally lived in the UK; in 1997, Dawn Brooke conceived a son at age 59.
These women may seem extreme outliers, almost freaks of nature; in the US, for example, the average age of first-time mothers is 26. A few decades from now, though, the sight of 70-year-old first-time mothers may not even raise eyebrows, say futurists.
“We could absolutely have more 70-year-old mothers because we are learning how to regulate the aging process better,” says Andrew Hessel, a microbiologist and geneticist, who cowrote "The Genesis Machine," a book about “rewriting life in the age of synthetic biology,” with Amy Webb, the futurist who recently wondered why 70-year-old women shouldn’t give birth.
Technically, we're already doing this, says Hessel, pointing to a technique known as in vitro gametogenesis (IVG). IVG refers to turning adult cells into sperm or egg cells. “You can think of it as the upgrade to IVF,” Hessel says. These vanguard stem cell research technologies can take even skin cells and turn them into induced pluripotent stem cells (iPSCs), which are basically master cells capable of maturing into any human cell, be it kidney cells, liver cells, brain cells or gametes, aka eggs and sperm, says Henry T. “Hank” Greely, a Stanford law professor who specializes in ethical, legal, and social issues in biosciences.
Mothers over 70 will be a minor blip, statistically speaking, Greely predicts.
In 2016, Greely wrote "The End of Sex," a book in which he described the science of making gametes out of iPSCs in detail. Greely says science will indeed enable us to see 70-year-old new mums fraternize with mothers several decades younger at kindergartens in the (not far) future. And it won’t be that big of a deal.
“An awful lot of children all around the world have been raised by grandmothers for millennia. To have 70-year-olds and 30-year-olds mingling in maternal roles is not new,” he says. That said, he doubts that many women will want to have a baby in the eighth decade of their life, even if science allows it. “Having a baby and raising a child is hard work. Even if 1% of all mothers are over 65, they aren’t going to change the world,” Greely says. Mothers over 70 will be a minor blip, statistically speaking, he predicts. But one thing is certain: the technology is here.
And more technologies for the same purpose could be on the way. In March 2021, researchers from Monash University in Melbourne, Australia, published research in Nature, where they successfully reprogrammed skin cells into a three-dimensional cellular structure that was morphologically and molecularly similar to a human embryo–the iBlastoid. In compliance with Australian law and international guidelines referencing the “primitive streak rule," which bans the use of embryos older than 14 days in scientific research, Monash scientists stopped growing their iBlastoids in vitro on day 11.
“The research was both cutting-edge and controversial, because it essentially created a new human life, not for the purpose of a patient who's wanting to conceive, but for basic research,” says Lindsay Wu, a senior lecturer in the School of Medical Sciences at the University of New South Wales (UNSW), in Kensington, Australia. If you really want to make sure what you are breeding is an embryo, you need to let it develop into a viable baby. “This is the real proof in the pudding,'' says Wu, who runs UNSW’s Laboratory for Ageing Research. Then you get to a stage where you decide for ethical purposes you have to abort it. “Fiddling here a bit too much?” he asks. Wu believes there are other approaches to tackling declining fertility due to older age that are less morally troubling.
He is actually working on them. Why would it be that women, who are at peak physical health in almost every other regard, in their mid- to late- thirties, have problems conceiving, asked Wu and his team in a research paper published in 2020 in Cell Reports. The simple answer is the egg cell. An average girl in puberty has between 300,000 and 400,000 eggs, while at around age 37, the same woman has only 25,000 eggs left. Things only go downhill from there. So, what torments the egg cells?
The UNSW team found that the levels of key molecules called NAD+ precursors, which are essential to the metabolism and genome stability of egg cells, decline with age. The team proceeded to add these vitamin-like substances back into the drinking water of reproductively aged, infertile lab mice, which then had babies.
“It's an important proof of concept,” says Wu. He is investigating how safe it is to replicate the experiment with humans in two ongoing studies. The ultimate goal is to restore the quality of egg cells that are left in patients in their late 30s and early- to mid-40s, says Wu. He sees the goal of getting pregnant for this age group as less ethically troubling, compared to 70-year-olds.
But what is ethical, anyway? “It is a tricky word,” says Hessel. He differentiates between ethics, which represent a personal position and may, thus, be more transient, and morality, longer lasting principles embraced across society such as, “Thou shalt not kill.” Unprecedented advances often bring out fear and antagonism until time passes and they just become…ordinary. When IVF pioneer Landrum Shettles tried to perform IVF in 1973, the chairman of Columbia’s College of Physicians and Surgeons interdicted the procedure at the last moment. Almost all countries in the world have IVF clinics today, and the global IVF services market is clearly a growth industry.
Besides, you don’t have a baby at 70 by accident: you really want it, Greely and Hessel agree. And by that age, mothers may be wiser and more financially secure, Hessel says (though he is quick to add that even the pregnancy of his own wife, who had her child at 40, was a high-risk one).
As a research question, figuring out whether older mothers are better than younger ones and vice-versa entails too many confounding variables, says Greely. And why should we focus on who’s the better mother anyway? “We've had 70-year-old and 80-year-old fathers forever–why should people have that much trouble getting used to mothers doing the same?” Greely wonders. For some women having a child at an old(er) age would be comforting; maybe that’s what matters.
And the technology to enable older women to have children is already here or coming very soon. That, perhaps, matters even more. Researchers have already created mice–and their offspring–entirely from scratch in the lab. “Doing this to produce human eggs is similar," says Hessel. "It is harder to collect tissues, and the inducing cocktails are different, but steady advances are being made." He predicts that the demand for fertility treatments will keep financing research and development in the area. He says that big leaps will be made if ethical concerns don’t block them: it is not far-fetched to believe that the first baby produced from lab-grown eggs will be born within the next decade.
In an op-ed in 2020 with Stat, Greely argued that we’ve already overcome the technical barrier for human cloning, but no one's really talking about it. Likewise, scientists are also working on enabling 70-year-old women to have babies, says Hessel, but most commentators are keeping really quiet about it. At least so far.
Podcast: The Friday Five weekly roundup in health research
The Friday Five covers five stories in health research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Covered in this week's Friday Five:
- Sex differences in cancer
- Promising research on a vaccine for Lyme disease
- Using a super material for brain-like devices
- Measuring your immunity to Covid
- Reducing dementia risk with leisure activities
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.