Researchers find declining antibody levels in SARS-CoV-2 patients are offset by T cells and B cells that remain behind to fight off reinfection
Questions remain regarding how long antibodies produced by a COVID-19 vaccine or natural infection will provide ongoing protection against SARS-CoV-2. However, a new study showing COVID-19 immunity may be “robust” and “long lasting” may signal important news for clinical laboratories and in vitro diagnostics companies developing serological tests for the coronavirus disease.
The LJI research team analyzed blood samples from 188 COVID-19 patients, 7% of whom had been hospitalized. They measured not only virus-specific antibodies in the blood stream, but also memory B cell infections, T helper cells, and cytotoxic (killer) T cells.
While antibodies eventually disappear from the blood stream, T cells and B cells appear to remain to fight future reinfection.
“As far as we know, this is the largest study ever for any acute infection that has measured all four of those components of immune memory,” Crotty said in a La Jolla Institute news release.
The LJI researchers found that virus-specific antibodies remained in the blood stream months after infection while spike-specific memory B cells—which could trigger an accelerated and robust antibody-mediated immune response in the event of reinfection—actually increased in the body after six months. In addition, COVID-19 survivors had an army of T cells ready to halt reinfection.
“Our data show immune memory in at least three immunological compartments was measurable in ~95% of subjects five to eight months post symptom onset, indicating that durable immunity against secondary COVID-19 disease is a possibility in most individuals,” the study concludes. The small percentage of the population found not to have long-lasting immunity following COVID-19 infection could be vaccinated in an effort to stop reinfection from occurring on the way to achieving herd immunity, the LJI researchers maintained.
Do COVID-19 Vaccines Create Equal Immunity Against Reinfection?
Whether COVID-19 vaccinations will provide the same immune response as an active infection has yet to be determined, but indications are protection may be equally strong.
“It is possible that immune memory will be similarly long lasting similar following vaccination, but we will have to wait until the data come in to be able to tell for sure,”
LJI Research Professor Daniela Weiskopf, PhD, said in the LJI statement. “Several months ago, our studies showed that natural infection induced a strong response, and this study now shows that the response lasts. The vaccine studies are at the initial stages, and so far, have been associated with strong protection. We are hopeful that a similar pattern of responses lasting over time will also emerge for the vaccine-induced responses.”
The study’s authors cautioned that people previously diagnosed with COVID-19 should not assume they have protective immunity from reinfection, the Washington Post noted. In fact, according to the LJI news release, researchers saw a “100-fold range in the magnitude of immune memory.”
Alessandro Sette, Doctor of Biological Sciences (above), an Italian immunologist, Professor at the Center for Autoimmunity and Inflammation/Center for Infectious Disease and Vaccine Research at La Jolla Institute for Immunology, and co-author of the study, told the Washington Post that people should act responsibly. “If I had COVID, I would still not throw away my masks, I would not go to rave parties … It’s like driving a car where you know you have 90% probability that the brakes work.” (Photo copyright: La Jolla Institute for Immunology.)
Previous Studies Found Little Natural Immunity Against SARS-CoV-2 Reinfection
The Scientist reported that several widely publicized previous studies raised concerns that immunity from natural infection was fleeting, perhaps dwindling in weeks or months. And a United Kingdom study published in Nature Microbiology found that COVID-19 generated “only a transient neutralizing antibody response that rapidly wanes” in patients who exhibited milder infection.
Daniel M. Davis, PhD, Professor of Immunology at the University of Manchester, says more research is needed before scientists can know for certain how long COVID-19 immunity lasts after natural infection.
“Overall, these results are interesting and provocative, but more research is needed, following large numbers of people over time. Only then, will we clearly know how many people produce antibodies when infected with coronavirus, and for how long,” Davis told Newsweek.
While additional peer-reviewed studies on the body’s immune response to COVID-19 will be needed, this latest study from the La Jolla Institute for Immunity may help guide clinical laboratories and in vitro diagnostic companies that are developing serological antibody tests for COVID-19 and lead to more definitive answers as to how long antibodies confer protective immunity.
By training a computer to analyze blood samples, and then automating the expert assessment process, the AI processed months’ worth of blood samples in a single day
New technologies and techniques for acquiring and transporting biological samples for clinical laboratory testing receive much attention. But what of the quality of the samples themselves? Blood products are expensive, as hospital medical laboratories that manage blood banks know all too well. Thus, any improvement to how labs store blood products and confidently determine their viability for transfusion is useful.
One such improvement is coming out of Canada. Researchers at the University of Alberta (U of A) in collaboration with scientists and academic institutions in five countries are looking into ways artificial intelligence (AI) and deep learning can be used to efficiently and quickly analyze red blood cells (RBCs). The results of the study may alter the way donated blood is evaluated and selected for transfusion to patients, according to an article in Folio, a U of A publication, titled, “AI Could Lead to Faster, Better Analysis of Donated Blood, Study Shows.”
Improving Blood Diagnostics through Precision Medicine and Deep Learning
“This project is an excellent example of how we are using our world-class expertise in precision health to contribute to the interdisciplinary work required to make fundamental changes in blood diagnostics,” said Jason Acker, PhD, a senior scientist at Canadian Blood Services’ Centre for Innovation, Professor of Laboratory Medicine and Pathology at the University of Alberta, and one of the lead authors of the study, in the Folio article.
The research took more than three years to complete and involved 19 experts from 12 academic institutions and blood collection facilities located in Canada, Germany, Switzerland, the United Kingdom, and the US.
“Our study shows that artificial intelligence gives us better information about the red blood cell morphology, which is the study of how these cells are shaped, much faster than human experts,” said Jason Acker, PhD (above), Senior Research Scientist, Canadian Blood Services, and Professor of Laboratory Medicine and Pathology at the University of Alberta, in an article published on the Canadian Blood Services website. “We anticipate this technology will improve diagnostics for clinicians as well as quality assurance for blood operators such as Canadian Blood Services in the coming years,” he added. Clinical laboratories in the US may also benefit from this new blood viability process. (Photo copyright: University of Alberta.)
To perform the study, the scientists first collected and manually categorized 52,000 red blood cell images. Those images were then used to train an algorithm that mimics the way a human mind works. The computer system was next tasked with analyzing the shape of RBCs for quality purposes.
Removing Human Bias from RBC Classification
“I was happy to collaborate with a group of people with diverse backgrounds and expertise,” said Tracey Turner, a senior research assistant in Acker’s laboratory and one of the authors of the study, in a Canadian Blood Services (CBS) article. “Annotating and reviewing over 52,000 images took a long time, however, it allowed me to see firsthand how much bias there is in manual classification of cell shape by humans and the benefit machine classification could bring.”
According to the CBS article, a red blood cell lasts about 115 days in the human body and the shape of the RBC reveals its age. Newer, healthier RBCs are shaped like discs with smooth edges. As they age, those edges become jagged and the cell eventually transforms into a sphere and loses the ability to perform its duty of transporting oxygen throughout the body.
Blood donations are processed, packed, and stored for later use. Once outside the body, the RBCs begin to change their shape and deteriorate. RBCs can only be stored for a maximum of 42 days before they lose the ability to function properly when transfused into a patient.
Scientists routinely examine the shape of RBCs to assess the quality of the cell units for transfusion to patients and, in some cases, diagnose and assess individuals with certain disorders and diseases. Typically, microscope examinations of red blood cells are performed by experts in medical laboratories to determine the quality of the stored blood. The RBCs are classified by shape and then assigned a morphology index score. This can be a complex, time-consuming, and laborious process.
“One of the amazing things about machine learning is that it allows us to see relationships we wouldn’t otherwise be able to see,” Acker said. “We categorize the cells into the buckets we’ve identified, but when we categorize, we take away information.”
Human analysis, apparently, is subjective and different professionals can arrive at different results after examining the same blood samples.
“Machines are naive of bias, and AI reveals some characteristics we wouldn’t have identified and is able to place red blood cells on a more nuanced spectrum of change in shape,” Acker explained.
The researchers discovered that the AI could accurately analyze and categorize the quality of the red blood cells. This ability to perform RBC morphology assessment could have critical implications for transfusion medicine.
“The computer actually did a better job than we could, and it was able to pick up subtle differences in a way that we can’t as humans,” Acker said.
“It’s not surprising that the red cells don’t just go from one shape to another. This computer showed that there’s actually a gradual progression of shape in samples from blood products, and it’s able to better classify these changes,” he added. “It radically changes the speed at which we can make these assessments of blood product quality.”
More Precision Matching Blood Donors to Recipients
According to the World Health Organization (WHO), approximately 118.5 million blood donations are collected globally each year. There is a considerable contrast in the level of access to blood products between high- and low-income nations, which makes accurate assessment of stored blood even more critical. About 40% of all blood donations are collected in high-income countries that home to only about 16% of the world’s population.
More studies and clinical trials will be necessary to determine if U of A’s approach to using AI to assess the quality of RBCs can safely transfer to clinical use. But these early results promise much in future precision medicine treatments.
“What this research is leading us to is the fact that we have the ability to be much more precise in how we match blood donors and recipients based on specific characteristics of blood cells,” Acker stated. “Through this study we have developed machine learning tools that are going to help inform how this change in clinical practice evolves.”
The AI tools being developed at the U of A could ultimately benefit patients as well as blood collection centers, and at hospitals where clinical laboratories typically manage the blood banking services, by making the process of matching transfusion recipients to donors more precise and ultimately safer.
Called the Geographic Direct Contracting Model (GEO), CMS’ new “voluntary payment model” aims at giving providers of Medicare Part A and Part B services “a direct incentive to improve care across entire geographic regions,” according to a CMS press release.
“The Geographic Direct Contracting Model is part of the Innovation Center’s suite of Direct Contracting models and is one of the Center’s largest bets to date on value-based care,” Brad Smith, Deputy Administrator and Director of the Center for Medicare and Medicaid Innovation (CMMI), told RevCycleIntelligence. Smith is also the former CEO and co-founder of Aspire Health.
According to a CMS Fact Sheet, the GEO model “will test whether a geographic-based approach to value-based care can improve quality of care and reduce costs for Medicare beneficiaries across an entire geographic region.”
“This model allows participating entities to build integrated relationships with healthcare providers and invest in population health in a region to better coordinate care, improve quality, and lower the cost of care for Medicare beneficiaries in a community, said CMS Administrator Seema Verma in the CMS press release. Clinical laboratories may find opportunities as well, to collaborate with physicians in the clinical decision-making process. (Photo copyright: Business Insider.)
“Leveraging best practices and lessons learned from prior Innovation Center models, Geo will enable Direct Contracting Entities (DCEs) to build integrated relationships with healthcare providers and community organizations in a region to better coordinate care and address the clinical and social needs of Medicare beneficiaries,” the CMS Fact Sheet states.
“If we’re successful, we’ll move value-based care from something that might be 10 or 20% of somebody’s revenue to something that’s 80 or hopefully 100% of somebody’s revenue (in five to 10 years),” Smith told MedPage Today.
Healthcare providers and health plans that participate in the Geographic Direct Contracting model must be covered entities under the Health Insurance and Portability Accountability Act (HIPAA) and submit applications by April 2, 2021, the CMS fact sheet states.
The first performance period starts Jan. 1, 2022, and participation is voluntary. Direct contracting entities take “100% shared savings and shared losses for Medicare Part A and B services for aligned Medicare fee for service beneficiaries in a defined region,” the CMS fact sheet explained.
CMS is considering implementing the GEO model in Atlanta, Dallas, Denver, Detroit, Houston, Los Angeles, Miami, Minneapolis, Orlando, Phoenix, Philadelphia, Pittsburgh, Riverside, San Diego, and Tampa.
“By initially testing the model in a small number of geographies, we will be able to thoughtfully learn how these flexibilities are able to impact quality and costs,” Smith told RevCycleIntelligence.
How Will Value-Based Care Programs Affect Clinical Laboratories?
Value-based payment arrangements require doctors to accept changes to how they are reimbursed for their services. In kind, doctors are examining how clinical laboratories can take on an enhanced role in clinical decision making.
“Physicians and hospitals in a value-based environment need a different level of service and professional consultation from the lab and pathology group because they are being incented to detect disease earlier and be active in managing patients with chronic conditions to keep them healthy and out of the hospital,” said Robert Michel, Publisher and Editor-in-Chief of Dark Daily and its sister publication The Dark Report.
Michel explained that value-based care providers are calling on labs to go beyond reporting accurate test results within allotted turnaround times. “They want collaboration in identifying at-risk patients and in finding and closing gaps in care by using laboratory test results.”
Medical laboratory leaders may want to reach out to healthcare providers participating in value-based care models to explore areas of interest relating to patient population, chronic conditions, and severity of illness.
Clinical laboratories that offer testing and reporting and additionally collaborate with healthcare providers and health plans in ways that contribute to improved patient outcomes and lowered costs, may be in a position to earn any financial rewards from these and other new value-based arrangements.
Available funds and disease prevalence affect whether pooled testing is feasible and desirable, notes University of Kansas Health System microbiology laboratory director
Pooled testing for the SARS-CoV-2 coronavirus has its supporters and its critics. There is no one-size-fits-all when it comes to pooling multiple patients’ biological samples into a single COVID-19 test in the hopes that the result will be all negative. Several factors must be in place for COVID-19 pooled testing to be viable at individual clinical laboratories. The experience of medical labs that considered doing pooled testing are informative.
For example, when Rachael Liesman, PhD, Director of Microbiology in Pathology and Laboratory Medicine at the University of Kansas Health System in Kansas City, researched developing a plan for pooled testing of COVID-19 patients for her health system, she found the strategy less than ideal for two reasons:
First was the rate of infection in the population being tested. If the rate was too high, pooled testing produced too many positive results, making the process impractical.
Second was the need for expensive automated equipment in the microbiology laboratory, the funding to buy that equipment, and the room to accommodate it.
Last summer, as Liesman and her microbiology lab staff were evaluating pooled testing, she spoke with Dark Daily’s sister publication The Dark Report. “We were trying to decide whether pooled testing really would save us anything,” she said in the exclusive interview. “We were looking at the barriers and trying to understand what we’d gain and what we’d lose.”
Deciding Against Pooled Testing at University of Kansas Health System
After careful consideration, the lab staff stopped considering pooled testing due to increased prevalence in the community, Liesman said in December. “Our positivity rate is double what we were seeing in the summer,” she noted.
“Of course, the biggest challenge with pooling specimens is you have to have a patient population that has a low enough virus prevalence to make it worth your time,” she noted. “For us, there may be some patient populations that have a low enough level of prevalence, but not enough to make pooling feasible.”
University of Kansas Health System’s microbiology laboratory has been running 800 to 1,000 COVID-19 molecular tests 24 hours a day, seven days a week, although the lab runs fewer tests on the weekends. On Jan. 8, the number of new coronavirus cases in Kansas was at 1,780 per million, according to the COVID Tracking Project (CTP). That was about the highest rate since the pandemic began early in the year.
“Pooling makes a lot of sense for massive high-volume reference labs. I don’t have the capacity to run 6,000 specimens a day through my lab,” noted Rachael Liesman, PhD (above), Director of Microbiology in Pathology and Laboratory Medicine at the University of Kansas Health System, in an exclusive interview with The Dark Report. Not all clinical laboratories can or should consider pooled testing, which Dark Daily noted in previous e-briefings. (Photo copyright: Twitter.)
“One of the challenges in any lab is when you get specimens arriving in volume of say 100 or 200 specimens every few hours,” Liesman explained. “When that happens, you have to determine rapidly which of those specimens you would want to pool and which of them you wouldn’t pool. Or, if you had the right circumstances, you could pool all of them.
“You might have asymptomatic patients in one group and symptomatic patients in another group. So, then you could put all samples from one group into a pool. But if you’re not set up that way, just figuring that part out could be really time consuming,” she noted.
“Another challenge,” Liesman added, “is if your laboratory doesn’t have liquid handlers, which are the instruments that do the pooling for you.”
Manual versus Automated Pooling
In a clinical laboratory without liquid handlers, the task of pooling is not automated and instead requires staff to do the work manually—one specimen and one pool at a time.
Without the right equipment, Liesman noted, somebody in the lab must physically take five tubes and combine them in into one tube. And that one person has to ensure the test tube of pooled specimens is appropriately stickered. Then, once that is completed, the information must be input into the laboratory information system (LIS).
“We have a liquid handler because we purchased one from Hamilton specifically for COVID testing. But getting all that information into the computer system can take a lot of time,” she said. “A lot of labs don’t have access to this type of instrumentation, which means the process becomes very hands-on.
“We already see repetitive-use injuries, and if many of your staff are spending their eight-hour shifts doing pipetting motions, then they’re at greater risk for repetitive-use injury,” she added.
In addition, having humans doing repetitive motions in a clinical laboratory increases the risk of specimen-handling errors such as tubes being mislabeled or misplaced. “Those mistakes are very hard to find,” Liesman noted. “For us, we’ve been asking if we have the resources to do pooling successfully. And, if we put all these resources into it, what do we gain? That’s the big question for us.”
For a clinical laboratory to successfully initiate and maintain an effective program for pooled testing of the SARS-CoV-2 coronavirus, it must have specific equipment available to reduce manual touches of the specimens and automate as many work processes as possible. The lab’s manager must also consider the staffing required to handle pooled testing. Even then, if disease prevalence climbs above a certain level, pooled testing will not be a viable solution.
These are the reasons why many medical laboratories have considered a pooled testing arrangement but decided it would not be appropriate for their organization. Meanwhile, at other clinical labs pooled SARS-CoV-2 testing has been a major success, partly because it enables the labs to test many more patients using the same quantity of test kits and related supplies.
The No Surprises Act, passed as part of the COVID-19 relief package, ensures patients do not receive surprise bills after out-of-network care, including hospital-based physicians such as pathologists
Consumer demand for price transparency in healthcare has been gaining support in Congress after several high-profile cases involving surprise medical billing received widespread reporting. Dark Daily covered many of these cases over the years.
Now, after initial opposition and months of legislative wrangling, organizations representing medical laboratories and clinical pathologists have expressed support for new federal legislation that aims to protect patients from surprise medical bills, including for clinical pathology and anatomic pathology services.
The new law Congress passed is known as the No Surprises Act (H.R.3630) and is part of the $900 billion COVID relief and government funding package signed by President Trump on December 27.
The law addresses the practice of “balance billing,” in which patients receive surprise bills for out-of-network medical services even when they use in-network providers. An ASCP policy statement noted that “a patient (consumer) may receive a bill for an episode of care or service they believed to be in-network and therefore covered by their insurance, but was in fact out-of-network.” This, according to the ASCP, “occurs most often in emergency situations, but specialties like pathology, radiology, and anesthesiology are affected as well.”
Most portions of the No Surprises Act take effect on January 1, 2022. The law prohibits balance billing for emergency care, air ambulance transport, or, in most cases, non-emergency care from in-network providers. Instead, if a patient unknowingly receives services from an out-of-network provider, they are liable only for co-pays and deductibles they would have paid for in-network care.
New Law Bars Pathologists from Balance Billing without Advance Patient Consent
The law permits balance billing under some circumstances, but only if the patient gives advance consent. And some specialties, including pathologists, are barred entirely from balance billing.
The law also establishes a process for determining how healthcare providers are reimbursed when a patient receives out-of-network care. The specifics of that process proved to be a major sticking point for providers. In states that have their own surprise-billing protections, payment will generally be determined by state law. Otherwise, payers and providers have 30 days to negotiate payment. If they can’t agree, payment is determined by an arbiter as part of an independent dispute resolution (IDR) process.
Early Proposal Drew Opposition
An early proposal to prohibit surprise billing drew opposition from a wide range of medical societies, including the ASCP, CAP, and the American Medical Association (AMA).
All were signatories to a July 29, 2020, letter sent to leaders of the US Senate and House of Representatives urging them to hold off from enacting surprise billing protections as part of COVID relief legislation. Though the groups agreed in principle with the need to protect patients from surprise billing, they contended that the proposed legislation leaned too heavily in favor of insurers, an ASCP news release noted.
“Legislative proposals that would dictate a set payment rate for unanticipated out-of-network care are neither market-based nor equitable, and do not account for the myriad inputs that factor into payment negotiations between insurers and providers,” the letter stated. “These proposals will only incentivize insurers to further narrow their provider networks and would also result in a massive financial windfall for insurers. As such, we oppose the setting of a payment rate in statute and are particularly concerned by proposals that would undermine hospitals and front-line caregivers during the COVID-19 pandemic.”
On December 11, leaders of key House and Senate committees announced agreement on a bipartisan draft of the bill that appeared to address these concerns, including establishment of the arbitration process for resolving payment disputes.
However, in a letter sent to the committee chairs and ranking members, the AHA asked for changes in the dispute-resolution provisions, including a prohibition on considering Medicare or Medicaid rates during arbitration. “We are concerned that the IDR process may be skewed if the arbiter is able to consider public payer reimbursement rates, which are well known to be below the cost of providing care,” the association stated. However, legislators agreed to the change after last-minute negotiations.
“The AHA is pleased that Congress rejected approaches that would impose arbitrary rates on providers, which could have significant consequences far beyond the scope of surprise medical bills and impact access to hospital care,” AHA President and CEO Rick Pollack (above) said in a statement. “We also applaud Congress for rejecting attempts to base rates on public payers.” (Photo copyright: American Hospital Association.)
Dispute Resolution for Pathologists
The CAP also expressed support for the final bill. In a statement, CAP noted that “As the legislation evolved during the 116th Congress, CAP members met with their federal lawmakers to discuss the CAP’s policy priorities.
“Through the CAP’s engagement and collaboration with other physician associations, the legislation improved drastically,” the CAP stated. “Specifically, the CAP lobbied Congress to hold patients harmless, establish a fair reimbursement formula for services provided, deny insurers the ability to dictate payment, create an independent dispute resolution (IDR) process that pathologists can participate in, and require network adequacy standards for health insurers.”
As laboratory testing was identified by thousands of respondents to the University of Chicago survey as the top surprise bill, it is likely that billing and transparency in charges for clinical pathologist and anatomic pathologist will continue to be scrutinized by law makers and healthcare associations.
With improved genetic sequencing comes larger human genome databases that could lead to new diagnostic and therapeutic biomarkers for clinical laboratories
As the COVID-19 pandemic grabbed headlines, the human genome database at the US Department of Veterans Affairs Million Veterans Program (MVP) quietly grew. Now, this wealth of genomic information—as well as data from other large-scale genomic and genetic collections—is expected to produce new biomarkers for clinical laboratory diagnostics and testing.
In December, cancer genomics company Personalis, Inc. (NASDAQ:PSNL) of Menlo Park, Calif., achieved a milestone and delivered its 100,000th whole human genome sequence to the MVP, according to a news release, which also states that Personalis is the sole sequencing provider to the MVP.
The VA’s MVP program, which started in 2011, has 850,000 enrolled veterans and is expected to eventually involve two million people. The VA’s aim is to explore the role genes, lifestyle, and military experience play in health and human illness, notes the VA’s MVP website.
Health conditions affecting veterans the MVP is researching include:
The VA has contracted with Personalis through September 2021, and has invested $175 million, Clinical OMICS reported. Personalis has earned approximately $14 million from the VA. That’s about 76% of the company’s revenue, according to 2nd quarter data, Clinical OMICS noted.
“The VA MVP is the largest whole genome sequencing project in the United States, and this is a significant milestone for both the program and for Personalis,” said John West (above with wife Judy), Founder and CEO of Personalis, in the news release. “Population-scale sequencing projects of this nature represent a cornerstone in our effort to accelerate the advancement of precision medicine across a wide range of disease areas,” he added. (Photo copyright: MIT Technology Review.)
Database of Veterans’ Genomes Used in Current Research
What has the VA gained from their investment so far? An MVP fact sheet states researchers are tapping MVP data for these and other veteran health-related studies:
Differentiating between prostate cancer tumors that require treatment and others that are slow-growing and not life-threatening.
How genetics drives obesity, diabetes, and heart disease.
How data in DNA translates into actual physiological changes within the body.
Gene variations and patients’ response to Warfarin.
NIH Research Program Studies Effects of Genetics on Health
Another research program, the National Institutes of Health’s All of Us study, recently began returning results to its participants who provided blood, urine, and/or saliva samples. The NIH aims to aid research into health outcomes influenced by genetics, environment, and lifestyle, explained a news release. The program, launched in 2018, has biological samples from more than 270,000 people with a goal of one million participants.
“We’re changing the paradigm for research. Participants are our most important partners in this effort, and we know many of them are eager to get their genetic results and learn about the science they’re making possible,” said Josh Denny, MD, CEO of the NIH’s All of Us research program in the news release. Denny, a physician scientist, was Professor of Biomedical Informatics and Medicine, Director of the Center for Precision Medicine and Vice President for Personalized Medicine at Vanderbilt University Medical Center prior to joining the NIH. (Photo copyright: National Institutes of Health.)
Inclusive Data Could Aid Precision Medicine
The news release notes that more than 80% of biological samples in the All of Us database come from people in communities that have been under-represented in biomedical research.
“We need programs like All of Us to build diverse datasets so that research findings ultimately benefit everyone,” said Brad Ozenberger, PhD, All of Us Genomics Program Director, in the news release.
Precision medicine designed for specific healthcare populations is a goal of the All of Us program.
“[All of Us is] beneficial to all Americans, but actually beneficial to the African American race because a lot of research and a lot of medicines that we are taking advantage of today, [African Americans] were not part of the research,” Chris Crawford, All of US Research Study Navigator, told the Birmingham Times. “As [the All of Us study] goes forward and we get a big diverse group of people, it will help as far as making medicine and treatment that will be more precise for us,” he added.
Large Databases Could Advance Care
Genome sequencing technology continues to improve. It is faster, less complicated, and cheaper to sequence a whole human genome than ever before. And the resulting sequence is more accurate.
Thus, as human genome sequencing databases grow, researchers are deriving useful scientific insights from the data. This is relevant for clinical laboratories because the new insights from studying bigger databases of genomic information will produce new diagnostic and therapeutic biomarkers that can be the basis for new clinical laboratory tests as well as useful diagnostic assays for anatomic pathologists.
