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.
Two years, six million deaths and still counting, scientists are searching for answers to prevent another COVID-19-like tragedy from ever occurring again. And it’s a gargantuan task.
Our disturbed ecosystems are creating more favorable conditions for the spread of infectious disease. Global warming, deforestation, rising sea levels and flooding have contributed to a rise in mosquito-borne infections and longer tick seasons. Disease-carrying animals are in closer range to other species and humans as they migrate to escape the heat. Bats are thought to have carried the SARS-CoV-2 virus to Wuhan, either directly or through another host animal, but thousands of novel viruses are lurking within other wild creatures.
Understanding how climate change contributes to the spread of disease is critical in predicting and thwarting future calamities. But the problem is that predictive models aren’t yet where they need to be for forecasting with certainty beyond the next year, as we could for weather, for instance.
The association between climate and infectious disease is poorly understood, says Irina Tezaur, a computational scientist at Sandia National Laboratories. “Correlations have been observed but it’s not known if these correlations translate to causal relationships.”
To make accurate longer-term predictions, scientists need more empirical data, multiple datasets specific to locations and diseases, and the ability to calculate risks that depend on unpredictable nature and human behavior. Another obstacle is that climate scientists and epidemiologists are not collaborating effectively, so some researchers are calling for a multidisciplinary approach, a new field called Outbreak Science.
Climate scientists are far ahead of epidemiologists in gathering essential data.
Earth System Models—combining the interactions of atmosphere, ocean, land, ice and biosphere—have been in place for two decades to monitor the effects of global climate change. These models must be combined with epidemiological and human model research, areas that are easily skewed by unpredictable elements, from extreme weather events to public environmental policy shifts.
“There is never just one driver in tracking the impact of climate on infectious disease,” says Joacim Rocklöv, a professor at the Heidelberg Institute of Global Health & Heidelberg Interdisciplinary Centre for Scientific Computing in Germany. Rocklöv has studied how climate affects vector-borne diseases—those transmitted to humans by mosquitoes, ticks or fleas. “You need to disentangle the variables to find out how much difference climate makes to the outcome and how much is other factors.” Determinants from deforestation to population density to lack of healthcare access influence the spread of disease.
Even though climate change is not the primary driver of infectious disease today, it poses a major threat to public health in the future, says Rocklöv.
The promise of predictive modeling
“Models are simplifications of a system we’re trying to understand,” says Jeremy Hess, who directs the Center for Health and the Global Environment at University of Washington in Seattle. “They’re tools for learning that improve over time with new observations.”
Accurate predictions depend on high-quality, long-term observational data but models must start with assumptions. “It’s not possible to apply an evidence-based approach for the next 40 years,” says Rocklöv. “Using models to experiment and learn is the only way to figure out what climate means for infectious disease. We collect data and analyze what already happened. What we do today will not make a difference for several decades.”
To improve accuracy, scientists develop and draw on thousands of models to cover as many scenarios as possible. One model may capture the dynamics of disease transmission while another focuses on immunity data or ocean influences or seasonal components of a virus. Further, each model needs to be disease-specific and often location-specific to be useful.
“All models have biases so it’s important to use a suite of models,” Tezaur stresses.
The modeling scientist chooses the drivers of change and parameters based on the question explored. The drivers could be increased precipitation, poverty or mosquito prevalence, for instance. Later, the scientist may need to isolate the effect of one driver so that will require another model.
There have been some related successes, such as the latest models for mosquito-borne diseases like Dengue, Zika and malaria as well as those for flu and tick-borne diseases, says Hess.
Rocklöv was part of a research team that used test data from 2018 and 2019 to identify regions at risk for West Nile virus outbreaks. Using AI, scientists were able to forecast outbreaks of the virus for the entire transmission season in Europe. “In the end, we want data-driven models; that’s what AI can accomplish,” says Rocklöv. Other researchers are making an important headway in creating a framework to predict novel host–parasite interactions.
Modeling studies can run months, years or decades. “The scientist is working with layers of data. The challenge is how to transform and couple different models together on a planetary scale,” says Jeanne Fair, a scientist at Los Alamos National Laboratory, Biosecurity and Public Health, in New Mexico.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts a tall spatial and temporal resolutions.
And it’s a constantly changing picture. A modeling study in an April 2022 issue of Nature predicted that thousands of animals will migrate to cooler locales as temperatures rise. This means that various species will come into closer contact with people and other mammals for the first time. This is likely to increase the risk of emerging infectious disease transmitted from animals to humans, especially in Africa and Asia.
Other things can happen too. Global warming could precipitate viral mutations or new infectious diseases that don’t respond to antimicrobial treatments. Insecticide-resistant mosquitoes could evolve. Weather-related food insecurity could increase malnutrition and weaken people’s immune systems. And the impact of an epidemic will be worse if it co-occurs during a heatwave, flood, or drought, says Hess.
The devil is in the climate variables
Solid predictions about the future of climate and disease are not possible with so many uncertainties. Difficult-to-measure drivers must be added to the empirical model mix, such as land and water use, ecosystem changes or the public’s willingness to accept a vaccine or practice social distancing. Nor is there any precedent for calculating the effect of climate changes that are accelerating at a faster speed than ever before.
The most critical climate variables thought to influence disease spread are temperature, precipitation, humidity, sunshine and wind, according to Tezaur’s research. And then there are variables within variables. Influenza scientists, for example, found that warm winters were predictors of the most severe flu seasons in the following year.
The human factor may be the most challenging determinant. To what degree will people curtail greenhouse gas emissions, if at all? The swift development of effective COVID-19 vaccines was a game-changer, but will scientists be able to repeat it during the next pandemic? Plus, no model could predict the amount of internet-fueled COVID-19 misinformation, Fair noted. To tackle this issue, infectious disease teams are looking to include more sociologists and political scientists in their modeling.
Addressing the gaps
Currently, researchers are focusing on the near future, predicting for next year, says Fair. “When it comes to long-term, that’s where we have the most work to do.” While scientists cannot foresee how political influences and misinformation spread will affect models, they are positioned to make headway in collecting and assessing new data streams that have never been merged.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions, Fair and her co-authors stated in their recent study. For example real-time data on mosquito prevalence and diversity in various settings and times is limited or non-existent. Fair also would like to see standards set in mosquito data collection in every country. “Standardizing across the US would be a huge accomplishment,” she says.
Understanding how climate change contributes to the spread of disease is critical for thwarting future calamities.
Jeanne Fair
Hess points to a dearth of data in local and regional datasets about how extreme weather events play out in different geographic locations. His research indicates that Africa and the Middle East experienced substantial climate shifts, for example, but are unrepresented in the evidentiary database, which limits conclusions. “A model for dengue may be good in Singapore but not necessarily in Port-au-Prince,” Hess explains. And, he adds, scientists need a way of evaluating models for how effective they are.
The hope, Rocklöv says, is that in the future we will have data-driven models rather than theoretical ones. In turn, sharper statistical analyses can inform resource allocation and intervention strategies to prevent outbreaks.
Most of all, experts emphasize that epidemiologists and climate scientists must stop working in silos. If scientists can successfully merge epidemiological data with climatic, biological, environmental, ecological and demographic data, they will make better predictions about complex disease patterns. Modeling “cross talk” and among disciplines and, in some cases, refusal to release data between countries is hindering discovery and advances.
It’s time for bold transdisciplinary action, says Hess. He points to initiatives that need funding in disease surveillance and control; developing and testing interventions; community education and social mobilization; decision-support analytics to predict when and where infections will emerge; advanced methodologies to improve modeling; training scientists in data management and integrated surveillance.
Establishing a new field of Outbreak Science to coordinate collaboration would accelerate progress. Investment in decision-support modeling tools for public health teams, policy makers, and other long-term planning stakeholders is imperative, too. We need to invest in programs that encourage people from climate modeling and epidemiology to work together in a cohesive fashion, says Tezaur. Joining forces is the only way to solve the formidable challenges ahead.
This article originally appeared in One Health/One Planet, a single-issue magazine that explores how climate change and other environmental shifts are increasing vulnerabilities to infectious diseases by land and by sea. The magazine probes how scientists are making progress with leaders in other fields toward solutions that embrace diverse perspectives and the interconnectedness of all lifeforms and the planet.
Scientists use AI to predict how hospital stays will go
The Friday Five covers five stories in 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.
Here are the promising studies covered in this week's Friday Five:
- The problem with bedtime munching
- Scientists use AI to predict how stays in hospitals will go
- How to armor the shields of our livers against cancer
- One big step to save the world: turn one kind of plastic into another
- The perfect recipe for tiny brains
And an honorable mention this week: Bigger is better when it comes to super neurons in super agers