Is Finding Out Your Baby’s Genetics A New Responsibility of Parenting?
Hours after a baby is born, its heel is pricked with a lancet. Drops of the infant's blood are collected on a porous card, which is then mailed to a state laboratory. The dried blood spots are screened for around thirty conditions, including phenylketonuria (PKU), the metabolic disorder that kick-started this kind of newborn screening over 60 years ago. In the U.S., parents are not asked for permission to screen their child. Newborn screening programs are public health programs, and the assumption is that no good parent would refuse a screening test that could identify a serious yet treatable condition in their baby.
Learning as much as you can about your child's health might seem like a natural obligation of parenting. But it's an assumption that I think needs to be much more closely examined.
Today, with the introduction of genome sequencing into clinical medicine, some are asking whether newborn screening goes far enough. As the cost of sequencing falls, should parents take a more expansive look at their children's health, learning not just whether they have a rare but treatable childhood condition, but also whether they are at risk for untreatable conditions or for diseases that, if they occur at all, will strike only in adulthood? Should genome sequencing be a part of every newborn's care?
It's an idea that appeals to Anne Wojcicki, the founder and CEO of the direct-to-consumer genetic testing company 23andMe, who in a 2016 interview with The Guardian newspaper predicted that having newborns tested would soon be considered standard practice—"as critical as testing your cholesterol"—and a new responsibility of parenting. Wojcicki isn't the only one excited to see everyone's genes examined at birth. Francis Collins, director of the National Institutes of Health and perhaps the most prominent advocate of genomics in the United States, has written that he is "almost certain … that whole-genome sequencing will become part of new-born screening in the next few years." Whether that would happen through state-mandated screening programs, or as part of routine pediatric care—or perhaps as a direct-to-consumer service that parents purchase at birth or receive as a baby-shower gift—is not clear.
Learning as much as you can about your child's health might seem like a natural obligation of parenting. But it's an assumption that I think needs to be much more closely examined, both because the results that genome sequencing can return are more complex and more uncertain than one might expect, and because parents are not actually responsible for their child's lifelong health and well-being.
What is a parent supposed to do about such a risk except worry?
Existing newborn screening tests look for the presence of rare conditions that, if identified early in life, before the child shows any symptoms, can be effectively treated. Sequencing could identify many of these same kinds of conditions (and it might be a good tool if it could be targeted to those conditions alone), but it would also identify gene variants that confer an increased risk rather than a certainty of disease. Occasionally that increased risk will be significant. About 12 percent of women in the general population will develop breast cancer during their lives, while those who have a harmful BRCA1 or BRCA2 gene variant have around a 70 percent chance of developing the disease. But for many—perhaps most—conditions, the increased risk associated with a particular gene variant will be very small. Researchers have identified over 600 genes that appear to be associated with schizophrenia, for example, but any one of those confers only a tiny increase in risk for the disorder. What is a parent supposed to do about such a risk except worry?
Sequencing results are uncertain in other important ways as well. While we now have the ability to map the genome—to create a read-out of the pairs of genetic letters that make up a person's DNA—we are still learning what most of it means for a person's health and well-being. Researchers even have a name for gene variants they think might be associated with a disease or disorder, but for which they don't have enough evidence to be sure. They are called "variants of unknown (or uncertain) significance (VUS), and they pop up in most people's sequencing results. In cancer genetics, where much research has been done, about 1 in 5 gene variants are reclassified over time. Most are downgraded, which means that a good number of VUS are eventually designated benign.
While one parent might reasonably decide to learn about their child's risk for a condition about which nothing can be done medically, a different, yet still thoroughly reasonable, parent might prefer to remain ignorant so that they can enjoy the time before their child is afflicted.
Then there's the puzzle of what to do about results that show increased risk or even certainty for a condition that we have no idea how to prevent. Some genomics advocates argue that even if a result is not "medically actionable," it might have "personal utility" because it allows parents to plan for their child's future needs, to enroll them in research, or to connect with other families whose children carry the same genetic marker.
Finding a certain gene variant in one child might inform parents' decisions about whether to have another—and if they do, about whether to use reproductive technologies or prenatal testing to select against that variant in a future child. I have no doubt that for some parents these personal utility arguments are persuasive, but notice how far we've now strayed from the serious yet treatable conditions that motivated governments to set up newborn screening programs, and to mandate such testing for all.
Which brings me to the other problem with the call for sequencing newborn babies: the idea that even if it's not what the law requires, it's what good parents should do. That idea is very compelling when we're talking about sequencing results that show a serious threat to the child's health, especially when interventions are available to prevent or treat that condition. But as I have shown, many sequencing results are not of this type.
While one parent might reasonably decide to learn about their child's risk for a condition about which nothing can be done medically, a different, yet still thoroughly reasonable, parent might prefer to remain ignorant so that they can enjoy the time before their child is afflicted. This parent might decide that the worry—and the hypervigilence it could inspire in them—is not in their child's best interest, or indeed in their own. This parent might also think that it should be up to the child, when he or she is older, to decide whether to learn about his or her risk for adult-onset conditions, especially given that many adults at high familial risk for conditions like Alzheimer's or Huntington's disease choose never to be tested. This parent will value the child's future autonomy and right not to know more than they value the chance to prepare for a health risk that won't strike the child until 40 or 50 years in the future.
Parents are not obligated to learn about their children's risk for a condition that cannot be prevented, has a small risk of occurring, or that would appear only in adulthood.
Contemporary understandings of parenting are famously demanding. We are asked to do everything within our power to advance our children's health and well-being—to act always in our children's best interests. Against that backdrop, the need to sequence every newborn baby's genome might seem obvious. But we should be skeptical. Many sequencing results are complex and uncertain. Parents are not obligated to learn about their children's risk for a condition that cannot be prevented, has a small risk of occurring, or that would appear only in adulthood. To suggest otherwise is to stretch parental responsibilities beyond the realm of childhood and beyond factors that parents can control.
There's a quiet revolution going on in medicine. It's driven by artificial intelligence, but paradoxically, new technology may put a more human face on healthcare.
AI's usefulness in healthcare ranges far and wide.
Artificial intelligence is software that can process massive amounts of information and learn over time, arriving at decisions with striking accuracy and efficiency. It offers greater accuracy in diagnosis, exponentially faster genome sequencing, the mining of medical literature and patient records at breathtaking speed, a dramatic reduction in administrative bureaucracy, personalized medicine, and even the democratization of healthcare.
The algorithms that bring these advantages won't replace doctors; rather, by offloading some of the most time-consuming tasks in healthcare, providers will be able to focus on personal interactions with patients—listening, empathizing, educating and generally putting the care back in healthcare. The relationship can focus on the alleviation of suffering, both the physical and emotional kind.
Challenges of Getting AI Up and Running
The AI revolution, still in its early phase in medicine, is already spurring some amazing advances, despite the fact that some experts say it has been overhyped. IBM's Watson Health program is a case in point. IBM capitalized on Watson's ability to process natural language by designing algorithms that devour data like medical articles and analyze images like MRIs and medical slides. The algorithms help diagnose diseases and recommend treatment strategies.
But Technology Review reported that a heavily hyped partnership with the MD Anderson Cancer Center in Houston fell apart in 2017 because of a lack of data in the proper format. The data existed, just not in a way that the voraciously data-hungry AI could use to train itself.
The hiccup certainly hasn't dampened the enthusiasm for medical AI among other tech giants, including Google and Apple, both of which have invested billions in their own healthcare projects. At this point, the main challenge is the need for algorithms to interpret a huge diversity of data mined from medical records. This can include everything from CT scans, MRIs, electrocardiograms, x-rays, and medical slides, to millions of pages of medical literature, physician's notes, and patient histories. It can even include data from implantables and wearables such as the Apple Watch and blood sugar monitors.
None of this information is in anything resembling a standard format across and even within hospitals, clinics, and diagnostic centers. Once the algorithms are trained, however, they can crunch massive amounts of data at blinding speed, with an accuracy that matches and sometimes even exceeds that of highly experienced doctors.
Genome sequencing, for example, took years to accomplish as recently as the early 2000s. The Human Genome Project, the first sequencing of the human genome, was an international effort that took 13 years to complete. In April of this year, Rady Children's Institute for Genomic Medicine in San Diego used an AI-powered genome sequencing algorithm to diagnose rare genetic diseases in infants in about 20 hours, according to ScienceDaily.
"Patient care will always begin and end with the doctor."
Dr. Stephen Kingsmore, the lead author of an article published in Science Translational Medicine, emphasized that even though the algorithm helped guide the treatment strategies of neonatal intensive care physicians, the doctor was still an indispensable link in the chain. "Some people call this artificial intelligence, we call it augmented intelligence," he says. "Patient care will always begin and end with the doctor."
One existing trend is helping to supply a great amount of valuable data to algorithms—the electronic health record. Initially blamed for exacerbating the already crushing workload of many physicians, the EHR is emerging as a boon for algorithms because it consolidates all of a patient's data in one record.
Examples of AI in Action Around the Globe
If you're a parent who has ever taken a child to the doctor with flulike symptoms, you know the anxiety of wondering if the symptoms signal something serious. Kang Zhang, M.D., Ph.D., the founding director of the Institute for Genomic Medicine at the University of California at San Diego, and colleagues developed an AI natural language processing model that used deep learning to analyze the EHRs of 1.3 million pediatric visits to a clinic in Guanzhou, China.
The AI identified common childhood diseases with about the same accuracy as human doctors, and it was even able to split the diagnoses into two categories—common conditions such as flu, and serious, life-threatening conditions like meningitis. Zhang has emphasized that the algorithm didn't replace the human doctor, but it did streamline the diagnostic process and could be used in a triage capacity when emergency room personnel need to prioritize the seriously ill over those suffering from common, less dangerous ailments.
AI's usefulness in healthcare ranges far and wide. In Uganda and several other African nations, AI is bringing modern diagnostics to remote villages that have no access to traditional technologies such as x-rays. The New York Times recently reported that there, doctors are using a pocket-sized, hand-held ultrasound machine that works in concert with a cell phone to image and diagnose everything from pneumonia (a common killer of children) to cancerous tumors.
The beauty of the highly portable, battery-powered device is that ultrasound images can be uploaded on computers so that physicians anywhere in the world can review them and weigh in with their advice. And the images are instantly incorporated into the patient's EHR.
Jonathan Rothberg, the founder of Butterfly Network, the Connecticut company that makes the device, told The New York Times that "Two thirds of the world's population gets no imaging at all. When you put something on a chip, the price goes down and you democratize it." The Butterfly ultrasound machine, which sells for $2,000, promises to be a game-changer in remote areas of Africa, South America, and Asia, as well as at the bedsides of patients in developed countries.
AI algorithms are rapidly emerging in healthcare across the U.S. and the world. China has become a major international player, set to surpass the U.S. this year in AI capital investment, the translation of AI research into marketable products, and even the number of often-cited research papers on AI. So far the U.S. is still the leader, but some experts describe the relationship between the U.S. and China as an AI cold war.
"The future of machine learning isn't sentient killer robots. It's longer human lives."
The U.S. Food and Drug Administration expanded its approval of medical algorithms from two in all of 2017 to about two per month throughout 2018. One of the first fields to be impacted is ophthalmology.
One algorithm, developed by the British AI company DeepMind (owned by Alphabet, the parent company of Google), instantly scans patients' retinas and is able to diagnose diabetic retinopathy without needing an ophthalmologist to interpret the scans. This means diabetics can get the test every year from their family physician without having to see a specialist. The Financial Times reported in March that the technology is now being used in clinics throughout Europe.
In Copenhagen, emergency service dispatchers are using a new voice-processing AI called Corti to analyze the conversations in emergency phone calls. The algorithm analyzes the verbal cues of callers, searches its huge database of medical information, and provides dispatchers with onscreen diagnostic information. Freddy Lippert, the CEO of EMS Copenhagen, notes that the algorithm has already saved lives by expediting accurate diagnoses in high-pressure situations where time is of the essence.
Researchers at the University of Nottingham in the UK have even developed a deep learning algorithm that predicts death more accurately than human clinicians. The algorithm incorporates data from a huge range of factors in a chronically ill population, including how many fruits and vegetables a patient eats on a daily basis. Dr. Stephen Weng, lead author of the study, published in PLOS ONE, said in a press release, "We found machine learning algorithms were significantly more accurate in predicting death than the standard prediction models developed by a human expert."
New digital technologies are allowing patients to participate in their healthcare as never before. A feature of the new Apple Watch is an app that detects cardiac arrhythmias and even produces an electrocardiogram if an abnormality is detected. The technology, approved by the FDA, is helping cardiologists monitor heart patients and design interventions for those who may be at higher risk of a cardiac event like a stroke.
If having an algorithm predict your death sends a shiver down your spine, consider that algorithms may keep you alive longer. In 2018, technology reporter Tristan Greene wrote for Medium that "…despite the unending deluge of panic-ridden articles declaring AI the path to apocalypse, we're now living in a world where algorithms save lives every day. The future of machine learning isn't sentient killer robots. It's longer human lives."
The Risks of AI Compiling Your Data
To be sure, the advent of AI-infused medical technology is not without its risks. One risk is that the use of AI wearables constantly monitoring our vital signs could turn us into a nation of hypochondriacs, racing to our doctors every time there's a blip in some vital sign. Such a development could stress an already overburdened system that suffers from, among other things, a shortage of doctors and nurses. Another risk has to do with the privacy protections on the massive repository of intimately personal information that AI will have on us.
In an article recently published in the Journal of the American Medical Association, Australian researcher Kit Huckvale and colleagues examined the handling of data by 36 smartphone apps that assisted people with either depression or smoking cessation, two areas that could lend themselves to stigmatization if they fell into the wrong hands.
Out of the 36 apps, 33 shared their data with third parties, despite the fact that just 25 of those apps had a privacy policy at all and out of those, only 23 stated that data would be shared with third parties. The recipients of all that data? It went almost exclusively to Facebook and Google, to be used for advertising and marketing purposes. But there's nothing to stop it from ending up in the hands of insurers, background databases, or any other entity.
Even when data isn't voluntarily shared, any digital information can be hacked. EHRs and even wearable devices share the same vulnerability as any other digital record or device. Still, the promise of AI to radically improve efficiency and accuracy in healthcare is hard to ignore.
AI Can Help Restore Humanity to Medicine
Eric Topol, director of the Scripps Research Translational Institute and author of the new book Deep Medicine, says that AI gives doctors and nurses the most precious gift of all: time.
Topol welcomes his patients' use of the Apple Watch cardiac feature and is optimistic about the ways that AI is revolutionizing medicine. He says that the watch helps doctors monitor how well medications are working and has already helped to prevent strokes. But in addition to that, AI will help bring the humanity back to a profession that has become as cold and hard as a stainless steel dissection table.
"When I graduated from medical school in the 1970s," he says, "you had a really intimate relationship with your doctor." Over the decades, he has seen that relationship steadily erode as medical organizations demanded that doctors see more and more patients within ever-shrinking time windows.
"Doctors have no time to think, to communicate. We need to restore the mission in medicine."
In addition to that, EHRs have meant that doctors and nurses are getting buried in paperwork and administrative tasks. This is no doubt one reason why a recent study by the World Health Organization showed that worldwide, about 50 percent of doctors suffer from burnout. People who are utterly exhausted make more mistakes, and medical clinicians are no different from the rest of us. Only medical mistakes have unacceptably high stakes. According to its website, Johns Hopkins University recently announced that in the U.S. alone, 250,000 people die from medical mistakes each year.
"Doctors have no time to think, to communicate," says Topol. "We need to restore the mission in medicine." AI is giving doctors more time to devote to the thing that attracted them to medicine in the first place—connecting deeply with patients.
There is a real danger at this juncture, though, that administrators aware of the time-saving aspects of AI will simply push doctors to see more patients, read more tests, and embrace an even more crushing workload.
"We can't leave it to the administrators to just make things worse," says Topol. "Now is the time for doctors to advocate for a restoration of the human touch. We need to stand up for patients and for the patient-doctor relationship."
AI could indeed be a game changer, he says, but rather than squander the huge benefits of more time, "We need a new equation going forward."
This Special Music Helped Preemie Babies’ Brains Develop
Move over, Baby Einstein: New research from Switzerland shows that listening to soothing music in the first weeks of life helps encourage brain development in preterm babies.
For the study, the scientists recruited a harpist and a new-age musician to compose three pieces of music.
The Lowdown
Children who are born prematurely, between 24 and 32 weeks of pregnancy, are far more likely to survive today than they used to be—but because their brains are less developed at birth, they're still at high risk for learning difficulties and emotional disorders later in life.
Researchers in Geneva thought that the unfamiliar and stressful noises in neonatal intensive care units might be partially responsible. After all, a hospital ward filled with alarms, other infants crying, and adults bustling in and out is far more disruptive than the quiet in-utero environment the babies are used to. They decided to test whether listening to pleasant music could have a positive, counterbalancing effect on the babies' brain development.
Led by Dr. Petra Hüppi at the University of Geneva, the scientists recruited Swiss harpist and new-age musician Andreas Vollenweider (who has collaborated with the likes of Carly Simon, Bryan Adams, and Bobby McFerrin). Vollenweider developed three pieces of music specifically for the NICU babies, which were played for them five times per week. Each track was used for specific purposes: To help the baby wake up; to stimulate a baby who was already awake; and to help the baby fall back asleep.
When they reached an age equivalent to a full-term baby, the infants underwent an MRI. The researchers focused on connections within the salience network, which determines how relevant information is, and then processes and acts on it—crucial components of healthy social behavior and emotional regulation. The neural networks of preemies who had listened to Vollenweider's pieces were stronger than preterm babies who had not received the intervention, and were instead much more similar to full-term babies.
Next Up
The first infants in the study are now 6 years old—the age when cognitive problems usually become diagnosable. Researchers plan to follow up with more cognitive and socio-emotional assessments, to determine whether the effects of the music intervention have lasted.
The first infants in the study are now 6 years old—the age when cognitive problems usually become diagnosable.
The scientists note in their paper that, while they saw strong results in the babies' primary auditory cortex and thalamus connections—suggesting that they had developed an ability to recognize and respond to familiar music—there was less reaction in the regions responsible for socioemotional processing. They hypothesize that more time spent listening to music during a NICU stay could improve those connections as well; but another study would be needed to know for sure.
Open Questions
Because this initial study had a fairly small sample size (only 20 preterm infants underwent the musical intervention, with another 19 studied as a control group), and they all listened to the same music for the same amount of time, it's still undetermined whether variations in the type and frequency of music would make a difference. Are Vollenweider's harps, bells, and punji the runaway favorite, or would other styles of music help, too? (Would "Baby Shark" help … or hurt?) There's also a chance that other types of repetitive sounds, like parents speaking or singing to their children, might have similar effects.
But the biggest question is still the one that the scientists plan to tackle next: Whether the intervention lasts as the children grow up. If it does, that's great news for any family with a preemie — and for the baby-sized headphone industry.