The Algorithm Will See You Now
Artificial intelligence in medicine, still in an early phase, stands to transform how doctors and nurses spend their time.
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."
Regenerative medicine has come a long way, baby
After a cloned baby sheep, what started as one of the most controversial areas in medicine is now promising to transform it.
The field of regenerative medicine had a shaky start. In 2002, when news spread about the first cloned animal, Dolly the sheep, a raucous debate ensued. Scary headlines and organized opposition groups put pressure on government leaders, who responded by tightening restrictions on this type of research.
Fast forward to today, and regenerative medicine, which focuses on making unhealthy tissues and organs healthy again, is rewriting the code to healing many disorders, though it’s still young enough to be considered nascent. What started as one of the most controversial areas in medicine is now promising to transform it.
Progress in the lab has addressed previous concerns. Back in the early 2000s, some of the most fervent controversy centered around somatic cell nuclear transfer (SCNT), the process used by scientists to produce Dolly. There was fear that this technique could be used in humans, with possibly adverse effects, considering the many medical problems of the animals who had been cloned.
But today, scientists have discovered better approaches with fewer risks. Pioneers in the field are embracing new possibilities for cellular reprogramming, 3D organ printing, AI collaboration, and even growing organs in space. It could bring a new era of personalized medicine for longer, healthier lives - while potentially sparking new controversies.
Engineering tissues from amniotic fluids
Work in regenerative medicine seeks to reverse damage to organs and tissues by culling, modifying and replacing cells in the human body. Scientists in this field reach deep into the mechanisms of diseases and the breakdowns of cells, the little workhorses that perform all life-giving processes. If cells can’t do their jobs, they take whole organs and systems down with them. Regenerative medicine seeks to harness the power of healthy cells derived from stem cells to do the work that can literally restore patients to a state of health—by giving them healthy, functioning tissues and organs.
Modern-day regenerative medicine takes its origin from the 1998 isolation of human embryonic stem cells, first achieved by John Gearhart at Johns Hopkins University. Gearhart isolated the pluripotent cells that can differentiate into virtually every kind of cell in the human body. There was a raging controversy about the use of these cells in research because at that time they came exclusively from early-stage embryos or fetal tissue.
Back then, the highly controversial SCNT cells were the only way to produce genetically matched stem cells to treat patients. Since then, the picture has changed radically because other sources of highly versatile stem cells have been developed. Today, scientists can derive stem cells from amniotic fluid or reprogram patients’ skin cells back to an immature state, so they can differentiate into whatever types of cells the patient needs.
In the context of medical history, the field of regenerative medicine is progressing at a dizzying speed. But for those living with aggressive or chronic illnesses, it can seem that the wheels of medical progress grind slowly.
The ethical debate has been dialed back and, in the last few decades, the field has produced important innovations, spurring the development of whole new FDA processes and categories, says Anthony Atala, a bioengineer and director of the Wake Forest Institute for Regenerative Medicine. Atala and a large team of researchers have pioneered many of the first applications of 3D printed tissues and organs using cells developed from patients or those obtained from amniotic fluid or placentas.
His lab, considered to be the largest devoted to translational regenerative medicine, is currently working with 40 different engineered human tissues. Sixteen of them have been transplanted into patients. That includes skin, bladders, urethras, muscles, kidneys and vaginal organs, to name just a few.
These achievements are made possible by converging disciplines and technologies, such as cell therapies, bioengineering, gene editing, nanotechnology and 3D printing, to create living tissues and organs for human transplants. Atala is currently overseeing clinical trials to test the safety of tissues and organs engineered in the Wake Forest lab, a significant step toward FDA approval.
In the context of medical history, the field of regenerative medicine is progressing at a dizzying speed. But for those living with aggressive or chronic illnesses, it can seem that the wheels of medical progress grind slowly.
“It’s never fast enough,” Atala says. “We want to get new treatments into the clinic faster, but the reality is that you have to dot all your i’s and cross all your t’s—and rightly so, for the sake of patient safety. People want predictions, but you can never predict how much work it will take to go from conceptualization to utilization.”
As a surgeon, he also treats patients and is able to follow transplant recipients. “At the end of the day, the goal is to get these technologies into patients, and working with the patients is a very rewarding experience,” he says. Will the 3D printed organs ever outrun the shortage of donated organs? “That’s the hope,” Atala says, “but this technology won’t eliminate the need for them in our lifetime.”
New methods are out of this world
Jeanne Loring, another pioneer in the field and director of the Center for Regenerative Medicine at Scripps Research Institute in San Diego, says that investment in regenerative medicine is not only paying off, but is leading to truly personalized medicine, one of the holy grails of modern science.
This is because a patient’s own skin cells can be reprogrammed to become replacements for various malfunctioning cells causing incurable diseases, such as diabetes, heart disease, macular degeneration and Parkinson’s. If the cells are obtained from a source other than the patient, they can be rejected by the immune system. This means that patients need lifelong immunosuppression, which isn’t ideal. “With Covid,” says Loring, “I became acutely aware of the dangers of immunosuppression.” Using the patient’s own cells eliminates that problem.
Microgravity conditions make it easier for the cells to form three-dimensional structures, which could more easily lead to the growing of whole organs. In fact, Loring's own cells have been sent to the ISS for study.
Loring has a special interest in neurons, or brain cells that can be developed by manipulating cells found in the skin. She is looking to eventually treat Parkinson’s disease using them. The manipulated cells produce dopamine, the critical hormone or neurotransmitter lacking in the brains of patients. A company she founded plans to start a Phase I clinical trial using cell therapies for Parkinson’s soon, she says.
This is the culmination of many years of basic research on her part, some of it on her own cells. In 2007, Loring had her own cells reprogrammed, so there’s a cell line that carries her DNA. “They’re just like embryonic stem cells, but personal,” she said.
Loring has another special interest—sending immature cells into space to be studied at the International Space Station. There, microgravity conditions make it easier for the cells to form three-dimensional structures, which could more easily lead to the growing of whole organs. In fact, her own cells have been sent to the ISS for study. “My colleagues and I have completed four missions at the space station,” she says. “The last cells came down last August. They were my own cells reprogrammed into pluripotent cells in 2009. No one else can say that,” she adds.
Future controversies and tipping points
Although the original SCNT debate has calmed down, more controversies may arise, Loring thinks.
One of them could concern growing synthetic embryos. The embryos are ultimately derived from embryonic stem cells, and it’s not clear to what stage these embryos can or will be grown in an artificial uterus—another recent invention. The science, so far done only in animals, is still new and has not been widely publicized but, eventually, “People will notice the production of synthetic embryos and growing them in an artificial uterus,” Loring says. It’s likely to incite many of the same reactions as the use of embryonic stem cells.
Bernard Siegel, the founder and director of the Regenerative Medicine Foundation and executive director of the newly formed Healthspan Action Coalition (HSAC), believes that stem cell science is rapidly approaching tipping point and changing all of medical science. (For disclosure, I do consulting work for HSAC). Siegel says that regenerative medicine has become a new pillar of medicine that has recently been fast-tracked by new technology.
Artificial intelligence is speeding up discoveries and the convergence of key disciplines, as demonstrated in Atala’s lab, which is creating complex new medical products that replace the body’s natural parts. Just as importantly, those parts are genetically matched and pose no risk of rejection.
These new technologies must be regulated, which can be a challenge, Siegel notes. “Cell therapies represent a challenge to the existing regulatory structure, including payment, reimbursement and infrastructure issues that 20 years ago, didn’t exist.” Now the FDA and other agencies are faced with this revolution, and they’re just beginning to adapt.
Siegel cited the 2021 FDA Modernization Act as a major step. The Act allows drug developers to use alternatives to animal testing in investigating the safety and efficacy of new compounds, loosening the agency’s requirement for extensive animal testing before a new drug can move into clinical trials. The Act is a recognition of the profound effect that cultured human cells are having on research. Being able to test drugs using actual human cells promises to be far safer and more accurate in predicting how they will act in the human body, and could accelerate drug development.
Siegel, a longtime veteran and founding father of several health advocacy organizations, believes this work helped bring cell therapies to people sooner rather than later. His new focus, through the HSAC, is to leverage regenerative medicine into extending not just the lifespan but the worldwide human healthspan, the period of life lived with health and vigor. “When you look at the HSAC as a tree,” asks Siegel, “what are the roots of that tree? Stem cell science and the huge ecosystem it has created.” The study of human aging is another root to the tree that has potential to lengthen healthspans.
The revolutionary science underlying the extension of the healthspan needs to be available to the whole world, Siegel says. “We need to take all these roots and come up with a way to improve the life of all mankind,” he says. “Everyone should be able to take advantage of this promising new world.”
Joy Milne's unusual sense of smell led Dr. Tilo Kunath, a neurobiologist at the Centre for Regenerative Medicine at the University of Edinburgh, and a host of other scientists, to develop a new diagnostic test for Parkinson's.
Forty years ago, Joy Milne, a nurse from Perth, Scotland, noticed a musky odor coming from her husband, Les. At first, Milne thought the smell was a result of bad hygiene and badgered her husband to take longer showers. But when the smell persisted, Milne learned to live with it, not wanting to hurt her husband's feelings.
Twelve years after she first noticed the "woodsy" smell, Les was diagnosed at the age of 44 with Parkinson's Disease, a neurodegenerative condition characterized by lack of dopamine production and loss of movement. Parkinson's Disease currently affects more than 10 million people worldwide.
Milne spent the next several years believing the strange smell was exclusive to her husband. But to her surprise, at a local support group meeting in 2012, she caught the familiar scent once again, hanging over the group like a cloud. Stunned, Milne started to wonder if the smell was the result of Parkinson's Disease itself.
Milne's discovery led her to Dr. Tilo Kunath, a neurobiologist at the Centre for Regenerative Medicine at the University of Edinburgh. Together, Milne, Kunath, and a host of other scientists would use Milne's unusual sense of smell to develop a new diagnostic test, now in development and poised to revolutionize the treatment of Parkinson's Disease.
"Joy was in the audience during a talk I was giving on my work, which has to do with Parkinson's and stem cell biology," Kunath says. "During the patient engagement portion of the talk, she asked me if Parkinson's had a smell to it." Confused, Kunath said he had never heard of this – but for months after his talk he continued to turn the question over in his mind.
Kunath knew from his research that the skin's microbiome changes during different disease processes, releasing metabolites that can give off odors. In the medical literature, diseases like melanoma and Type 2 diabetes have been known to carry a specific scent – but no such connection had been made with Parkinson's. If people could smell Parkinson's, he thought, then it stood to reason that those metabolites could be isolated, identified, and used to potentially diagnose Parkinson's by their presence alone.
First, Kunath and his colleagues decided to test Milne's sense of smell. "I got in touch with Joy again and we designed a protocol to test her sense of smell without her having to be around patients," says Kunath, which could have affected the validity of the test. In his spare time, Kunath collected t-shirt samples from people diagnosed with Parkinson's and from others without the diagnosis and gave them to Milne to smell. In 100 percent of the samples, Milne was able to detect whether a person had Parkinson's based on smell alone. Amazingly, Milne was even able to detect the "Parkinson's scent" in a shirt from the control group – someone who did not have a Parkinson's diagnosis, but would go on to be diagnosed nine months later.
From the initial study, the team discovered that Parkinson's did have a smell, that Milne – inexplicably – could detect it, and that she could detect it long before diagnosis like she had with her husband, Les. But the experiments revealed other things that the team hadn't been expecting.
"One surprising thing we learned from that experiment was that the odor was always located in the back of the shirt – never in the armpit, where we expected the smell to be," Kunath says. "I had a chance meeting with a dermatologist and he said the smell was due to the patient's sebum, which are greasy secretions that are really dense on your upper back. We have sweat glands, instead of sebum, in our armpits." Patients with Parkinson's are also known to have increased sebum production.
With the knowledge that a patient's sebum was the source of the unusual smell, researchers could go on to investigate exactly what metabolites were in the sebum and in what amounts. Kunath, along with his associate, Dr. Perdita Barran, collected and analyzed sebum samples from 64 participants across the United Kingdom. Once the samples were collected, Barran and others analyzed it using a method called gas chromatography mass spectrometry, or GS-MC, which separated, weighed and helped identify the individual compounds present in each sebum sample.
Barran's team can now correctly identify Parkinson's in nine out of 10 patients – a much quicker and more accurate way to diagnose than what clinicians do now.
"The compounds we've identified in the sebum are not unique to people with Parkinson's, but they are differently expressed," says Barran, a professor of mass spectrometry at the University of Manchester. "So this test we're developing now is not a black-and-white, do-you-have-something kind of test, but rather how much of these compounds do you have compared to other people and other compounds." The team identified over a dozen compounds that were present in the sebum of Parkinson's patients in much larger amounts than the control group.
Using only the GC-MS and a sebum swab test, Barran's team can now correctly identify Parkinson's in nine out of 10 patients – a much quicker and more accurate way to diagnose than what clinicians do now.
"At the moment, a clinical diagnosis is based on the patient's physical symptoms," Barran says, and determining whether a patient has Parkinson's is often a long and drawn-out process of elimination. "Doctors might say that a group of symptoms looks like Parkinson's, but there are other reasons people might have those symptoms, and it might take another year before they're certain," Barran says. "Some of those symptoms are just signs of aging, and other symptoms like tremor are present in recovering alcoholics or people with other kinds of dementia." People under the age of 40 with Parkinson's symptoms, who present with stiff arms, are often misdiagnosed with carpal tunnel syndrome, she adds.
Additionally, by the time physical symptoms are present, Parkinson's patients have already lost a substantial amount of dopamine receptors – about sixty percent -- in the brain's basal ganglia. Getting a diagnosis before physical symptoms appear would mean earlier interventions that could prevent dopamine loss and preserve regular movement, Barran says.
"Early diagnosis is good if it means there's a chance of early intervention," says Barran. "It stops the process of dopamine loss, which means that motor symptoms potentially will not happen, or the onset of symptoms will be substantially delayed." Barran's team is in the processing of streamlining the sebum test so that definitive results will be ready in just two minutes.
"What we're doing right now will be a very inexpensive test, a rapid-screen test, and that will encourage people to self-sample and test at home," says Barran. In addition to diagnosing Parkinson's, she says, this test could also be potentially useful to determine if medications were at a therapeutic dose in people who have the disease, since the odor is strongest in people whose symptoms are least controlled by medication.
"When symptoms are under control, the odor is lower," Barran says. "Potentially this would allow patients and clinicians to see whether their symptoms are being managed properly with medication, or perhaps if they're being overmedicated." Hypothetically, patients could also use the test to determine if interventions like diet and exercise are effective at keeping Parkinson's controlled.
"We hope within the next two to five years we will have a test available."
Barran is now running another clinical trial – one that determines whether they can diagnose at an earlier stage and whether they can identify a difference in sebum samples between different forms of Parkinson's or diseases that have Parkinson's-like symptoms, such as Lewy Body Dementia.
"Within the next one to two years, we hope to be running a trial in the Manchester area for those people who do not have motor symptoms but are at risk for developing dementia due to symptoms like loss of smell and sleep difficulty," Barran had said in 2019. "If we can establish that, we can roll out a test that determines if you have Parkinson's or not with those first pre-motor symptoms, and then at what stage. We hope within the next two to five years we will have a test available."
In a 2022 study, published in the American Chemical Society, researchers used mass spectrometry to analyze sebum from skin swabs for the presence of the specific molecules. They found that some specific molecules are present only in people who have Parkinson’s. Now they hope that the same method can be used in regular diagnostic labs. The test, many years in the making, is inching its way to the clinic.
"We would likely first give this test to people who are at risk due to a genetic predisposition, or who are at risk based on prodomal symptoms, like people who suffer from a REM sleep disorder who have a 50 to 70 percent chance of developing Parkinson's within a ten year period," Barran says. "Those would be people who would benefit from early therapeutic intervention. For the normal population, it isn't beneficial at the moment to know until we have therapeutic interventions that can be useful."
Milne's husband, Les, passed away from complications of Parkinson's Disease in 2015. But thanks to him and the dedication of his wife, Joy, science may have found a way to someday prolong the lives of others with this devastating disease. Sometimes she can smell people who have Parkinson’s while in the supermarket or walking down the street but has been told by medical ethicists she cannot tell them, Milne said in an interview with the Guardian. But once the test becomes available in the clinics, it will do the job for her.
[Ed. Note: A older version of this hit article originally ran on September 3, 2019.]