Pregnant & Breastfeeding Women Who Get the COVID-19 Vaccine Are Protecting Their Infants, Research Suggests
Becky Cummings had multiple reasons to get vaccinated against COVID-19 while tending to her firstborn, Clark, who arrived in September 2020 at 27 weeks.
The 29-year-old intensive care unit nurse in Greensboro, North Carolina, had witnessed the devastation day in and day out as the virus took its toll on the young and old. But when she was offered the vaccine, she hesitated, skeptical of its rapid emergency use authorization.
Exclusion of pregnant and lactating mothers from clinical trials fueled her concerns. Ultimately, though, she concluded the benefits of vaccination outweighed the risks of contracting the potentially deadly virus.
"Long story short," Cummings says, in December "I got vaccinated to protect myself, my family, my patients, and the general public."
At the time, Cummings remained on the fence about breastfeeding, citing a lack of evidence to support its safety after vaccination, so she pumped and stashed breast milk in the freezer. Her son is adjusting to life as a preemie, requiring mother's milk to be thickened with formula, but she's becoming comfortable with the idea of breastfeeding as more research suggests it's safe.
"If I could pop him on the boob," she says, "I would do it in a heartbeat."
Now, a study recently published in the Journal of the American Medical Association found "robust secretion" of specific antibodies in the breast milk of mothers who received a COVID-19 vaccine, indicating a potentially protective effect against infection in their infants.
The presence of antibodies in the breast milk, detectable as early as two weeks after vaccination, lasted for six weeks after the second dose of the Pfizer-BioNTech vaccine.
"We believe antibody secretion into breast milk will persist for much longer than six weeks, but we first wanted to prove any secretion at all after vaccination," says Ilan Youngster, the study's corresponding author and head of pediatric infectious diseases at Shamir Medical Center in Zerifin, Israel.
That's why the research team performed a preliminary analysis at six weeks. "We are still collecting samples from participants and hope to soon be able to comment about the duration of secretion."
As with other respiratory illnesses, such as influenza and pertussis, secretion of antibodies in breast milk confers protection from infection in infants. The researchers expect a similar immune response from the COVID-19 vaccine and are expecting the findings to spur an increase in vaccine acceptance among pregnant and lactating women.
A COVID-19 outbreak struck three families the research team followed in the study, resulting in at least one non-breastfed sibling developing symptomatic infection; however, none of the breastfed babies became ill. "This is obviously not empirical proof," Youngster acknowledges, "but still a nice anecdote."
Leaps.org inquired whether infants who derive antibodies only through breast milk are likely to have a lower immunity than infants whose mothers were vaccinated while they were in utero. In other words, is maternal transmission of antibodies stronger during pregnancy than during breastfeeding, or about the same?
"This is a different kind of transmission," Youngster explains. "When a woman is infected or vaccinated during pregnancy, some antibodies will be transferred through the placenta to the baby's bloodstream and be present for several months." But in the nursing mother, that protection occurs through local action. "We always recommend breastfeeding whenever possible, and, in this case, it might have added benefits."
A study published online in March found COVID-19 vaccination provided pregnant and lactating women with robust immune responses comparable to those experienced by their nonpregnant counterparts. The study, appearing in the American Journal of Obstetrics and Gynecology, documented the presence of vaccine-generated antibodies in umbilical cord blood and breast milk after mothers had been vaccinated.
Natali Aziz, a maternal-fetal medicine specialist at Stanford University School of Medicine, notes that it's too early to draw firm conclusions about the reduction in COVID-19 infection rates among newborns of vaccinated mothers. Citing the two aforementioned research studies, she says it's biologically plausible that antibodies passed through the placenta and breast milk impart protective benefits. While thousands of pregnant and lactating women have been vaccinated against COVID-19, without incurring adverse outcomes, many are still wondering whether it's safe to breastfeed afterward.
It's important to bear in mind that pregnant women may develop more severe COVID-19 complications, which could lead to intubation or admittance to the intensive care unit. "We, in our practice, are supporting pregnant and breastfeeding patients to be vaccinated," says Aziz, who is also director of perinatal infectious diseases at Stanford Children's Health, which has been vaccinating new mothers and other hospitalized patients at discharge since late April.
Earlier in April, Huntington Hospital in Long Island, New York, began offering the COVID-19 vaccine to women after they gave birth. The hospital chose the one-shot Johnson & Johnson vaccine for postpartum patients, so they wouldn't need to return for a second shot while acclimating to life with a newborn, says Mitchell Kramer, chairman of obstetrics and gynecology.
The hospital suspended the program when the Food and Drug Administration and the Centers for Disease Control and Prevention paused use of the J&J vaccine starting April 13, while investigating several reports of dangerous blood clots and low platelet counts among more than 7 million people in the United States who had received that vaccine.
In lifting the pause April 23, the agencies announced the vaccine's fact sheets will bear a warning of the heightened risk for a rare but serious blood clot disorder among women under age 50. As a result, Kramer says, "we will likely not be using the J&J vaccine for our postpartum population."
So, would it make sense to vaccinate infants when one for them eventually becomes available, not just their mothers? "In general, most of the time, infants do not have as good of an immune response to vaccines," says Jonathan Temte, associate dean for public health and community engagement at the University of Wisconsin School of Medicine and Public Health in Madison.
"Many of our vaccines are held until children are six months of age. For example, the influenza vaccine starts at age six months, the measles vaccine typically starts one year of age, as do rubella and mumps. Immune response is typically not very good for viral illnesses in young infants under the age of six months."
So far, the FDA has granted emergency use authorization of the Pfizer-BioNTech vaccine for children as young as 16 years old. The agency is considering data from Pfizer to lower that age limit to 12. Studies are also underway in children under age 12. Meanwhile, data from Moderna on 12-to 17-year-olds and from Pfizer on 12- to 15-year-olds have not been made public. (Pfizer announced at the end of March that its vaccine is 100 percent effective in preventing COVID-19 in the latter age group, and FDA authorization for this population is expected soon.)
"There will be step-wise progression to younger children, with infants and toddlers being the last ones tested," says James Campbell, a pediatric infectious diseases physician and head of maternal and child clinical studies at the University of Maryland School of Medicine Center for Vaccine Development.
"Once the data are analyzed for safety, tolerability, optimal dose and regimen, and immune responses," he adds, "they could be authorized and recommended and made available to American children." The data on younger children are not expected until the end of this year, with regulatory authorization possible in early 2022.
For now, Vonnie Cesar, a family nurse practitioner in Smyrna, Georgia, is aiming to persuade expectant and new mothers to get vaccinated. She has observed that patients in metro Atlanta seem more inclined than their rural counterparts.
To quell some of their skepticism and fears, Cesar, who also teaches nursing students, conceived a visual way to demonstrate the novel mechanism behind the COVID-19 vaccine technology. Holding a palm-size physical therapy ball outfitted with clear-colored push pins, she simulates the spiked protein of the coronavirus. Slime slathered at the gaps permeates areas around the spikes—a process similar to how our antibodies build immunity to the virus.
These conversations often lead hesitant patients to discuss vaccination with their husbands or partners. "The majority of people I'm speaking with," she says, "are coming to the conclusion that this is the right thing for me, this is the common good, and they want to make sure that they're here for their children."
CORRECTION: An earlier version of this article mistakenly stated that the COVID-19 vaccines were granted emergency "approval." They have been granted emergency use authorization, not full FDA approval. We regret the error.
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.