Genetically Sequencing Healthy Babies Yielded Surprising Results
Today in Melrose, Massachusetts, Cora Stetson is the picture of good health, a bubbly precocious 2-year-old. But Cora has two separate mutations in the gene that produces a critical enzyme called biotinidase and her body produces only 40 percent of the normal levels of that enzyme.
In the last few years, the dream of predicting and preventing diseases through genomics, starting in childhood, is finally within reach.
That's enough to pass conventional newborn (heelstick) screening, but may not be enough for normal brain development, putting baby Cora at risk for seizures and cognitive impairment. But thanks to an experimental study in which Cora's DNA was sequenced after birth, this condition was discovered and she is being treated with a safe and inexpensive vitamin supplement.
Stories like these are beginning to emerge from the BabySeq Project, the first clinical trial in the world to systematically sequence healthy newborn infants. This trial was led by my research group with funding from the National Institutes of Health. While still controversial, it is pointing the way to a future in which adults, or even newborns, can receive comprehensive genetic analysis in order to determine their risk of future disease and enable opportunities to prevent them.
Some believe that medicine is still not ready for genomic population screening, but others feel it is long overdue. After all, the sequencing of the Human Genome Project was completed in 2003, and with this milestone, it became feasible to sequence and interpret the genome of any human being. The costs have come down dramatically since then; an entire human genome can now be sequenced for about $800, although the costs of bioinformatic and medical interpretation can add another $200 to $2000 more, depending upon the number of genes interrogated and the sophistication of the interpretive effort.
Two-year-old Cora Stetson, whose DNA sequencing after birth identified a potentially dangerous genetic mutation in time for her to receive preventive treatment.
(Photo courtesy of Robert Green)
The ability to sequence the human genome yielded extraordinary benefits in scientific discovery, disease diagnosis, and targeted cancer treatment. But the ability of genomes to detect health risks in advance, to actually predict the medical future of an individual, has been mired in controversy and slow to manifest. In particular, the oft-cited vision that healthy infants could be genetically tested at birth in order to predict and prevent the diseases they would encounter, has proven to be far tougher to implement than anyone anticipated.
But in the last few years, the dream of predicting and preventing diseases through genomics, starting in childhood, is finally within reach. Why did it take so long? And what remains to be done?
Great Expectations
Part of the problem was the unrealistic expectations that had been building for years in advance of the genomic science itself. For example, the 1997 film Gattaca portrayed a near future in which the lifetime risk of disease was readily predicted the moment an infant is born. In the fanfare that accompanied the completion of the Human Genome Project, the notion of predicting and preventing future disease in an individual became a powerful meme that was used to inspire investment and public support for genomic research long before the tools were in place to make it happen.
Another part of the problem was the success of state-mandated newborn screening programs that began in the 1960's with biochemical tests of the "heel-stick" for babies with metabolic disorders. These programs have worked beautifully, costing only a few dollars per baby and saving thousands of infants from death and severe cognitive impairment. It seemed only logical that a new technology like genome sequencing would add power and promise to such programs. But instead of embracing the notion of newborn sequencing, newborn screening laboratories have thus far rejected the entire idea as too expensive, too ambiguous, and too threatening to the comfortable constituency that they had built within the public health framework.
"What can you find when you look as deeply as possible into the medical genomes of healthy individuals?"
Creating the Evidence Base for Preventive Genomics
Despite a number of obstacles, there are researchers who are exploring how to achieve the original vision of genomic testing as a tool for disease prediction and prevention. For example, in our NIH-funded MedSeq Project, we were the first to ask the question: "What can you find when you look as deeply as possible into the medical genomes of healthy individuals?"
Most people do not understand that genetic information comes in four separate categories: 1) dominant mutations putting the individual at risk for rare conditions like familial forms of heart disease or cancer, (2) recessive mutations putting the individual's children at risk for rare conditions like cystic fibrosis or PKU, (3) variants across the genome that can be tallied to construct polygenic risk scores for common conditions like heart disease or type 2 diabetes, and (4) variants that can influence drug metabolism or predict drug side effects such as the muscle pain that occasionally occurs with statin use.
The technological and analytical challenges of our study were formidable, because we decided to systematically interrogate over 5000 disease-associated genes and report results in all four categories of genetic information directly to the primary care physicians for each of our volunteers. We enrolled 200 adults and found that everyone who was sequenced had medically relevant polygenic and pharmacogenomic results, over 90 percent carried recessive mutations that could have been important to reproduction, and an extraordinary 14.5 percent carried dominant mutations for rare genetic conditions.
A few years later we launched the BabySeq Project. In this study, we restricted the number of genes to include only those with child/adolescent onset that could benefit medically from early warning, and even so, we found 9.4 percent carried dominant mutations for rare conditions.
At first, our interpretation around the high proportion of apparently healthy individuals with dominant mutations for rare genetic conditions was simple – that these conditions had lower "penetrance" than anticipated; in other words, only a small proportion of those who carried the dominant mutation would get the disease. If this interpretation were to hold, then genetic risk information might be far less useful than we had hoped.
Suddenly the information available in the genome of even an apparently healthy individual is looking more robust, and the prospect of preventive genomics is looking feasible.
But then we circled back with each adult or infant in order to examine and test them for any possible features of the rare disease in question. When we did this, we were surprised to see that in over a quarter of those carrying such mutations, there were already subtle signs of the disease in question that had not even been suspected! Now our interpretation was different. We now believe that genetic risk may be responsible for subclinical disease in a much higher proportion of people than has ever been suspected!
Meanwhile, colleagues of ours have been demonstrating that detailed analysis of polygenic risk scores can identify individuals at high risk for common conditions like heart disease. So adding up the medically relevant results in any given genome, we start to see that you can learn your risks for a rare monogenic condition, a common polygenic condition, a bad effect from a drug you might take in the future, or for having a child with a devastating recessive condition. Suddenly the information available in the genome of even an apparently healthy individual is looking more robust, and the prospect of preventive genomics is looking feasible.
Preventive Genomics Arrives in Clinical Medicine
There is still considerable evidence to gather before we can recommend genomic screening for the entire population. For example, it is important to make sure that families who learn about such risks do not suffer harms or waste resources from excessive medical attention. And many doctors don't yet have guidance on how to use such information with their patients. But our research is convincing many people that preventive genomics is coming and that it will save lives.
In fact, we recently launched a Preventive Genomics Clinic at Brigham and Women's Hospital where information-seeking adults can obtain predictive genomic testing with the highest quality interpretation and medical context, and be coached over time in light of their disease risks toward a healthier outcome. Insurance doesn't yet cover such testing, so patients must pay out of pocket for now, but they can choose from a menu of genetic screening tests, all of which are more comprehensive than consumer-facing products. Genetic counseling is available but optional. So far, this service is for adults only, but sequencing for children will surely follow soon.
As the costs of sequencing and other Omics technologies continue to decline, we will see both responsible and irresponsible marketing of genetic testing, and we will need to guard against unscientific claims. But at the same time, we must be far more imaginative and fast moving in mainstream medicine than we have been to date in order to claim the emerging benefits of preventive genomics where it is now clear that suffering can be averted, and lives can be saved. The future has arrived if we are bold enough to grasp it.
Funding and Disclosures:
Dr. Green's research is supported by the National Institutes of Health, the Department of Defense and through donations to The Franca Sozzani Fund for Preventive Genomics. Dr. Green receives compensation for advising the following companies: AIA, Applied Therapeutics, Helix, Ohana, OptraHealth, Prudential, Verily and Veritas; and is co-founder and advisor to Genome Medical, Inc, a technology and services company providing genetics expertise to patients, providers, employers and care systems.
The Science of Why Adjusting to Omicron Is So Tough
We are sticking our heads into the sand of reality on Omicron, and the results may be catastrophic.
Omicron is over 4 times more infectious than Delta. The Pfizer two-shot vaccine offers only 33% protection from infection. A Pfizer booster vaccine does raises protection to about 75%, but wanes to around 30-40 percent 10 weeks after the booster.
The only silver lining is that Omicron appears to cause a milder illness than Delta. Yet the World Health Organization has warned about the “mildness” narrative.
That’s because the much faster disease transmission and vaccine escape undercut the less severe overall nature of Omicron. That’s why hospitals have a large probability of being overwhelmed, as the Center for Disease Control warned, in this major Omicron wave.
Yet despite this very serious threat, we see the lack of real action. The federal government tightened international travel guidelines and is promoting boosters. Certainly, it’s crucial to get as many people to get their booster – and initial vaccine doses – as soon as possible. But the government is not taking the steps that would be the real game-changers.
Pfizer’s anti-viral drug Paxlovid decreases the risk of hospitalization and death from COVID by 89%. Due to this effectiveness, the FDA approved Pfizer ending the trial early, because it would be unethical to withhold the drug from people in the control group. Yet the FDA chose not to hasten the approval process along with the emergence of Omicron in late November, only getting around to emergency authorization in late December once Omicron took over. That delay meant the lack of Paxlovid for the height of the Omicron wave, since it takes many weeks to ramp up production, resulting in an unknown number of unnecessary deaths.
We humans are prone to falling for dangerous judgment errors called cognitive biases.
Widely available at-home testing would enable people to test themselves quickly, so that those with mild symptoms can quarantine instead of infecting others. Yet the federal government did not make tests available to patients when Omicron emerged in late November. That’s despite the obviousness of the coming wave based on the precedent of South Africa, UK, and Denmark and despite the fact that the government made vaccines freely available. Its best effort was to mandate that insurance cover reimbursements for these kits, which is way too much of a barrier for most people. By the time Omicron took over, the federal government recognized its mistake and ordered 500 million tests to be made available in January. However, that’s far too late. And the FDA also played a harmful role here, with its excessive focus on accuracy going back to mid-2020, blocking the widespread availability of cheap at-home tests. By contrast, Europe has a much better supply of tests, due to its approval of quick and slightly less accurate tests.
Neither do we see meaningful leadership at the level of employers. Some are bringing out the tired old “delay the office reopening” play. For example, Google, Uber, and Ford, along with many others, have delayed the return to the office for several months. Those that already returned are calling for stricter pandemic measures, such as more masks and social distancing, but not changing their work arrangements or adding sufficient ventilation to address the spread of COVID.
Despite plenty of warnings from risk management and cognitive bias experts, leaders are repeating the same mistakes we fell into with Delta. And so are regular people. For example, surveys show that Omicron has had very little impact on the willingness of unvaccinated Americans to get a first vaccine dose, or of vaccinated Americans to get a booster. That’s despite Omicron having taken over from Delta in late December.
What explains this puzzling behavior on both the individual and society level? We humans are prone to falling for dangerous judgment errors called cognitive biases. Rooted in wishful thinking and gut reactions, these mental blindspots lead to poor strategic and financial decisions when evaluating choices.
These cognitive biases stem from the more primitive, emotional, and intuitive part of our brains that ensured survival in our ancestral environment. This quick, automatic reaction of our emotions represents the autopilot system of thinking, one of the two systems of thinking in our brains. It makes good decisions most of the time but also regularly makes certain systematic thinking errors, since it’s optimized to help us survive. In modern society, our survival is much less at risk, and our gut is more likely to compel us to focus on the wrong information to make decisions.
One of the biggest challenges relevant to Omicron is the cognitive bias known as the ostrich effect. Named after the myth that ostriches stick their heads into the sand when they fear danger, the ostrich effect refers to people denying negative reality. Delta illustrated the high likelihood of additional dangerous variants, yet we failed to pay attention to and prepare for such a threat.
We want the future to be normal. We’re tired of the pandemic and just want to get back to pre-pandemic times. Thus, we greatly underestimate the probability and impact of major disruptors, like new COVID variants. That cognitive bias is called the normalcy bias.
When we learn one way of functioning in any area, we tend to stick to that way of functioning. You might have heard of this as the hammer-nail syndrome: when you have a hammer, everything looks like a nail. That syndrome is called functional fixedness. This cognitive bias causes those used to their old ways of action to reject any alternatives, including to prepare for a new variant.
Our minds naturally prioritize the present. We want what we want now, and downplay the long-term consequences of our current desires. That fallacious mental pattern is called hyperbolic discounting, where we excessively discount the benefits of orienting toward the future and focus on the present. A clear example is focusing on the short-term perceived gains of trying to return to normal over managing the risks of future variants.
The way forward into the future is to defeat cognitive biases and avoid denying reality by rethinking our approach to the future.
The FDA requires a serious overhaul. It’s designed for a non-pandemic environment, where the goal is to have a highly conservative, slow-going, and risk-averse approach so that the public feels confident trusting whatever it approved. That’s simply unacceptable in a fast-moving pandemic, and we are bound to face future pandemics in the future.
The federal government needs to have cognitive bias experts weigh in on federal policy. Putting all of its eggs in one basket – vaccinations – is not a wise move when we face the risks of a vaccine-escaping variant. Its focus should also be on expediting and prioritizing anti-virals, scaling up cheap rapid testing, and subsidizing high-filtration masks.
For employers, instead of dictating a top-down approach to how employees collaborate, companies need to adopt a decentralized team-led approach. Each individual team leader of a rank-and-file employee team should determine what works best for their team. After all, team leaders tend to know much more of what their teams need, after all. Moreover, they can respond to local emergencies like COVID surges.
At the same time, team leaders need to be trained to integrate best practices for hybrid and remote team leadership. Companies transitioned to telework abruptly as part of the March 2020 lockdowns. They fell into the cognitive bias of functional fixedness and transposed their pre-existing, in-office methods of collaboration on remote work. Zoom happy hours are a clear example: The large majority of employees dislike them, and research shows they are disconnecting, rather than connecting.
Yet supervisors continue to use them, despite the existence of much better methods of facilitating colalboration, which have been shown to work, such as virtual water cooler discussions, virtual coworking, and virtual mentoring. Leaders also need to facilitate innovation in hybrid and remote teams through techniques such as virtual asynchronous brainstorming. Finally, team leaders need to adjust performance evaluation to adapt to the needs of hybrid and remote teams.
On an individual level, people built up certain expectations during the first two years of the pandemic, and they don't apply with Omicron. For example, most people still think that a cloth mask is a fine source of protection. In reality, you really need an N-95 mask, since Omicron is so much more infectious. Another example is that many people don’t realize that symptom onset is much quicker with Omicron, and they aren’t prepared for the consequences.
Remember that we have a huge number of people who are asymptomatic, often without knowing it, due to the much higher mildness of Omicron. About 8% of people admitted to hospitals for other reasons in San Francisco test positive for COVID without symptoms, which we can assume translates for other cities. That means many may think they're fine and they're actually infectious. The result is a much higher chance of someone getting many other people sick.
During this time of record-breaking cases, you need to be mindful about your internalized assumptions and adjust your risk calculus accordingly. So if you can delay higher-risk activities, January and February might be the time to do it. Prepare for waves of disruptions to continue over time, at least through the end of February.
Of course, you might also choose to not worry about getting infected. If you are vaccinated and boosted, and do not have any additional health risks, you are very unlikely to have a serious illness due to Omicron. You can just take the small risk of a serious illness – which can happen – and go about your daily life. If doing so, watch out for those you care about who do have health concerns, since if you infect them, they might not have a mild case even with Omicron.
In short, instead of trying to turn back the clock to the lost world of January 2020, consider how we might create a competitive advantage in our new future. COVID will never go away: we need to learn to live with it. That means reacting appropriately and thoughtfully to new variants and being intentional about our trade-offs.
Picture this: your medical first responder descends from the sky like a friendly, unmanned starship. Hovering over your door, it drops a device with recorded instructions to help a bystander jumpstart your heart that has stopped. This, after the 911 call but before the ambulance arrives.
This is exactly what happened on Dec. 9, 2021, when a 71-year-old man in Sweden suffered a cardiac arrest while shoveling snow. A passerby, seeing him collapse, called for an ambulance. In just over three minutes, a drone swooped overhead carrying an Automated External Defibrillator (AED). The patient was revived on the spot before the ambulance arrived to rush him to the hospital where he made a full recovery. The revolutionary technology saved his life.
In 2020, Sweden became the first country to deploy drones carrying AEDs to people in sudden cardiac arrest, when survival odds depend on getting CPR and an electric shock to the heart from a defibrillator within 5 minutes—nearly always before emergency responders arrive.
In the U.S. alone, more than 356,00 cardiac arrests occur outside of hospitals each year; 9 out of 10 of these people die. Plus, the risk of permanent brain injury increases after the first three minutes the heart stops beating. After nine minutes, damage to the brain and other organs is usually severe and irreversible.
“The fundamental technology can be applied to a lot of other emergency situations.”
Once the stuff of sci fi, the delivery of life-saving medical equipment by drone will be commonplace in the near future, experts say. The Swedish team is hailing their study as the first-ever proof of concept for using drones in emergency medicine. The drones arrived only two minutes before the ambulance in most cases but that’s significant during cardiac arrest when survival rates drop 10% every minute.
Since that 2020 pilot, the drones have been tweaked for better performance. They can travel faster and after dark today, and route planning has been optimized, says Mats Sällström, chief executive officer of Everdrone, the technical and development guru for the project, who is collaborating with researchers at the Karolinska Institutet and Sweden’s national emergency call center, SOS Alarm.
When an emergency call comes in, the operator determines if it’s a cardiac arrest. If so, the caller gets CPR instructions while an ambulance is summoned and a control center is notified automatically to dispatch a drone. If conditions allow, the drone flies to the scene via a GPS signal from the caller’s cell phone. Once dropped at the location, the AED beeps to signal its arrival. The AED talks the user through every step when it’s opened while the emergency operator offers support.
Public health officials have tried placing AEDs in public spaces like airports and shopping malls for quick access but the results have been disappointing. Poor usage rates of 2% to 3% have been attributed to bystanders not knowing where they are, not wanting to leave victims, or the site being closed when needed.
Some people fear they could harm the victim or won’t know how to use the AED but not to worry, says Wayne Rosamond, a professor of epidemiology at the University of North Carolina Gillings School of Global Public Health, who studies AED drones. “[The device] won’t shock someone unless they need to be shocked,” he says.
The AED instructions are foolproof, echoes Timothy Chan, professor of engineering at the University of Toronto, who has been building optimization models to design drone networks in Ontario, Canada. All the same, he says, community education will be essential for success. “People have more awareness about drones than AEDs,” he’s found.
Rosamond and Chan are among scientists around the world inspired by Sweden to do their own modeling, simulation and feasibility studies on drone-delivered AEDs.
“Scandinavia is way ahead of us,” notes Rosamond. “There is a tremendous amount of regulatory control over flying drones in the U.S.” In addition to Federal Aviation Administration restrictions, medical drones in the U.S. must comply with HIPAA laws surrounding confidentiality and security of patient information.
To date, Sweden has expanded drone operations and home bases around the country and throughout Europe. Since April 2021, the team has deployed 1-4 drones per week, says Sällström.
Certain weather conditions remain an obstacle. The drones cannot be dispatched safely in rain, snow and heavy wind. Close, heavily populated neighborhoods with high-rise buildings also present challenges.
“Semi-urban areas with residential low-rise [1-5 stories] buildings are the sweet spot for our operations,” Sällström says. “However, as the system matures, we will pursue operations in practically all-weather conditions and also in densely populated areas.” The team is also trying to improve drone speed and battery life to enable flights to rural and remote areas in the future.
Chan predicts that delivering AEDs via drone will be a regular occurrence in five years. In addition, he says, “The fundamental technology can be applied to a lot of other emergency situations.”
Drones could carry medications for anaphylactic shock and opioid overdose, or bring tourniquets and bandages to trauma victims, Chan suggests. Other researchers are looking at the delivery of glucose for low blood sugar emergencies and the transport of organs for transplant.
The sky is no longer the limit.