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Scientists Use Thousands of Genetic Markers to Develop Risk Scores for Six Common Diseases: Findings May Have Implications for Clinical Laboratories

Study demonstrates how precision medicine is advancing because of new insights from the use and interpretation of whole-genome sequencing

As part of the Genomic Medicine at Veterans Affairs Study (GenoVA), researchers from Harvard Medical School, Veterans Affairs Boston Healthcare System, and Brigham and Women’s Hospital in Massachusetts used thousands of genetic markers to develop and validate polygenic risk scores (PRS) for six common illnesses. These findings may eventually provide clinical laboratories and anatomic pathology groups with useful biomarkers and diagnostic tests.

The focus of the ongoing GenoVA study is to “determine the clinical effectiveness of polygenic risk score testing among patients at high genetic risk for at least one of six diseases measured by time-to-diagnosis of prevalent or incident disease over 24 months,” according to the National Institutes of Health.   

The scientists used data obtained from 36,423 patients enrolled in the Mass General Brigham Biobank. The six diseases they researched were:

The polygenic scores were then tested among 227 healthy adult patients to determine their risk for the six diseases. The researchers found that:

  • 11% of the patients had a high-risk score for atrial fibrillation,
  • 7% for coronary artery disease,
  • 8% for diabetes, and
  • 6% for colorectal cancer.

Among the subjects used for the study:

  • 15% of the men in the study had a high-risk score for prostate cancer, and
  • 13% of the women in the study had a high score for breast cancer. 

The researchers concluded that the implementation of PRS may help improve disease prevention and management and give doctor’s a way to assess a patient’s risk for these conditions. They published their findings in the journal Nature Medicine, titled, “Development of a Clinical Polygenic Risk Score Assay and Reporting Workflow.”

“We have shown that [medical] laboratory assay development and PRS reporting to patients and physicians are feasible … As the performance of PRS continues to improve—particularly for individuals of underrepresented ancestry groups—the implementation processes we describe can serve as generalizable models for laboratories and health systems looking to realize the potential of PRS for improved patient health,” the researchers wrote.

Using PRS in Clinical Decision Support

Polygenetic risk scores examine multiple genetic markers for risk of certain diseases. A calculation based on hundreds or thousands of these genetic markers could help doctors and patients make personalized treatment decisions, a core tenet of precision medicine.

“As a primary care physician myself, I knew that busy physicians were not going to have time to take an entire course on polygenic risk scores. Instead, we wanted to design a lab report and informational resources that succinctly told the doctor and patient what they need to know to make a decision about using a polygenic risk score result in their healthcare,” epidemiologist Jason Vassy, MD, told The Harvard Gazette. Vassy is Associate Professor, Harvard Medical School at VA Boston Healthcare System and one of the authors of the research.

Jason Vassy, MD
“This is another great example of precision medicine,” Jason Vassy, MD (above), Adjunct Assistant Professor, General Internal Medicine at Boston University School of Medicine, told WebMD. “There’s always been a tantalizing idea that someone’s genetic makeup might help tailor preventative medicine and treatment.” Personalized clinical laboratory testing is increasingly becoming based on an individual’s genetics. (Photo copyright: Harvard Medical School.)

Increasing Diversity of Patients in Genomic Research

The team did encounter some challenges during their analysis. Because most existing genomic research was performed on persons of European descent, the risk scores are less accurate among non-European populations. The researchers for this study addressed this limitation by applying additional statistical methods to qualify accurate PRS calculations across multiple racial groups.

“Researchers must continue working to increase the diversity of patients participating in genomics research,” said Matthew Lebo, PhD, Chief Laboratory Director, Laboratory Molecular Medicine, at Mass General Brigham and one of the authors of the study. “In the meantime, we were heartened to see that we could generate and implement valid genetic scores for patients of diverse backgrounds,” he told The Harvard Gazette.

The team hopes the scores may be utilized in the future to help doctors and patients make better decisions regarding preventative care and screenings.

“It’s easy to say that everyone needs a colonoscopy at age 45,” Vassy told WebMD. “But what if you’re such a low risk that you could put it off for longer? We may get to the point where we understand risk so much that someone may not need one at all.”

Future of PRS in Clinical Decision Making

The scientists plan to enroll more than 1,000 patients in a new program and track them for two years to assess how medical professionals use PRS in clinical care. It is feasible that patients who are at high risk for certain diseases may opt for more frequent screenings or take preventative medicines to mitigate their risk.

“Getting to that point will take time,” Vassy added. “But I can see this type of information playing a role in shared decision making between doctor and patient in the near future.”

The team also established resources and educational materials to assist both doctors and patients in using the scores.

“It’s still very early days for precision prevention,” Vassy noted, “but we have shown it is feasible to overcome some of the first barriers to bringing polygenic risk scores into the clinic.”

More research and studies are needed to prove the effectiveness of using PRS tests in clinical care and determine its role in customized treatment plans based on personal genetics. Nevertheless, pathologists and medical scientists will want to follow the GenoVA study.  

“It is probably most helpful to think of polygenic risk scores as a risk factor for disease, not a diagnostic test or an indication that an individual will certainly develop the disease,” Vassy said. “Most diseases have complex, multifactorial etiologies, and a high polygenic risk score is just one piece of the puzzle.”

Pathologists and clinical laboratory managers may want to stay informed as researchers in the GenoVA study tease new useful diagnostic insights from their ongoing study of the whole human genome. Meanwhile, the GenoVA team is moving forward with the 1,000-patient study with the expectation that this new knowledge may enable earlier and more accurate diagnoses of the health conditions that were the focus of the GenoVA study.

JP Schlingman

Related Information:

Genetic Risk Scores Developed for Six Diseases

Development of a Clinical Polygenic Risk Score Assay and Reporting Workflow

What If You Knew Your Unique Risk for Every Disease?

Polygenic Risk Scores May Assist Decision-making in Primary Care

Research Study Shows Cardiac Ultrasound AI May Be Superior to Anatomic Pathologists at Predicting COVID-19 Death Risk

WASE-COVID Study also found that use of artificial intelligence technology minimized variability among echocardiogram scan results

Many physicians—including anatomic pathologists—are watching the development of artificial intelligence (AI)-powered diagnostic tools that are intended to analyze images and analyze the data with accuracy comparable to trained doctors. Now comes news of a recent study that demonstrated the ability of an AI tool to analyze echocardiograph images and deliver analyses equal to or better than trained physicians.

Conducted by researchers from the World Alliance Societies of Echocardiography and presented at the latest annual sessions of the American College of Cardiology (ACC), the WASE-COVID Study involved assessing the ability of the AI platform to analyze digital echocardiograph images with the goal of predicting mortality in patients with severe cases of COVID-19.

The findings could have widespread implications for the adoption of AI solutions that assist doctors in analyzing the full range of digital images used by radiologists, pathologists, and other specialist physicians. The researchers published their study in the Journal of the American Society of Echocardiography (JASE), titled, “Echocardiographic Correlates of In-Hospital Death in Patients with Acute COVID-19 Infection: The World Alliance Societies of Echocardiography (WASE-COVID) Study.”

To complete their research, the WASE-COVID Study scientists examined 870 patients with acute COVID-19 infection from 13 medical centers in nine countries throughout Asia, Europe, United States, and Latin America.

Human versus Artificial Intelligence Analysis

Echocardiograms were analyzed with automated, machine learning-derived algorithms to calculate various data points and identify echocardiographic parameters that would be prognostic of clinical outcomes in hospitalized patients. The results were then compared to human analysis.

All patients in the study had previously tested positive for COVID-19 infection using a polymerase chain reaction (PCR) or rapid antigen test (RAT) and received a clinically-indicated echocardiogram upon admission. For those patients ultimately discharged from the hospital, a follow-up echocardiogram was performed after three months.

“What we learned was that the manual tracings were not able to predict mortality,” Federico Asch, MD, FACC, FASE, Director of the Echocardiography Core Lab at MedStar Health Research Institute in Washington, DC, told US Cardiology Review in a video interview describing the WASE-COVID Study findings.

Asch is also Associate Professor of Medicine (Cardiology) at Georgetown University. He added, “But on the same echoes, if the analysis was done by machine—Ultromics EchoGo Core, a software that is commercially available—when we used the measurements obtained through this platform, we were able to predict in-hospital and out-of-hospital mortality both with ejection fraction and left ventricular longitudinal strain.”

Federico Asch, MD

“When compared to the manual reads, the AI algorithms had a much higher predictive value for mortality,” Federico Asch, MD (above), told US Cardiology Review. “Indeed, they were predictive where the manual ones were not.” These findings may have implications in the development and adoption of artificial intelligence driven clinical laboratory diagnostics and for predicting risk of COVID-19 deaths in hospitalized heart patients. Click here to review the entire video interview. (Photo copyright: US Cardiology Review.)

Nearly half of the 870 hospitalized patients were admitted to intensive care units, 27% were placed on ventilators, 188 patients died in the hospital, and 50 additional patients died within three to six months after being released from the hospital.

According to an Ultromics news release:

  • 10 of 13 medical centers performed limited cardiac exams as their primary COVID in-patient practice and three out of the 13 centers performed comprehensive exams.
  • In-hospital mortality rates ranged from 11% in Asia, 19% in Europe, 26% in the US, to 27% in Latin America.
  • Left ventricular longitudinal strain (LVLS), right ventricle free wall strain (RVFWS), as well as a patient’s age, lactic dehydrogenase levels and history of lung disease, were independently associated with mortality. Left ventricle ejection fraction (LVEF) was not.
  • Fully automated quantification of LVEF and LVLS using AI minimized variability.
  • AI-based left ventricular analyses, but not manual, were significant predictors of in-hospital and follow-up mortality.

The WASE-COVID Study also revealed the varying international use of cardiac ultrasound (echocardiography) on COVID-19 patients.

“By using machines, we reduce variability. By reducing variability, we have a better capacity to compare our results with other outcomes, whether that outcome in this case is mortality or it could be changes over time,” Asch stated in the US Cardiology Review video. “What this really means is that we may be able to show associations and comparisons by using AI that we cannot do with manual [readings] because manual has more variation and is less reliable.”

He said the next steps will be to see if the findings hold true when AI is used in other populations of cardiac patients.

COVID-19 Pandemic Increased Need for Swift Analyses

An earlier WASE Study in 2016 set out to answer whether normal left ventricular heart chamber quantifications vary across countries, geographical regions, and cultures. However, the data produced by that study took years to review. Asch said the COVID-19 pandemic created a need for such analysis to be done more quickly.

“When the pandemic began, we knew that the clinical urgency to learn as much as possible about the cardiovascular connection to COVID-19 was incredibly high, and that we had to find a better way of securely and consistently reviewing all of this information in a timely manner,” he said in the Ultromics new release.

Coronary artery disease (CAD) is the most common form of heart disease and affects more than 16.5 million people over the age of 20. By 2035, the economic burden of CAD will reach an estimated $749 billion in the US alone, according to the Ultromics website.

“COVID-19 has placed an even greater pressure on cardiac care and looks likely to have lasting implications in terms of its impact on the heart,” said Ross Upton, PhD, Founder and CEO of Oxford, UK-based Ultromics, in a news release announcing the US Food and Drug Administration’s 510(k) clearance for the EchoGo Pro, which supports clinicians’ diagnosing of CAD. “The healthcare industry needs to quickly pivot towards AI-powered automation to reduce the time to diagnosis and improve patient care.”

Use of AI to analyze digital pathology images is expected to be a fast-growing element in the anatomic pathology profession, particularly in the diagnosis of cancer. As Dark Daily outlined in this free white Paper, “Anatomic Pathology at the Tipping Point? The Economic Case for Adopting Digital Technology and AI Applications Now,” anatomic pathology laboratories can expect adoption of AI and digital technology to gain in popularity among pathologists in coming years.

—Andrea Downing Peck

Related Information:

Echocardiographic Correlates of In-Hospital Death in Patients with Acute COVID-19 Infection: The World Alliance Societies of Echocardiography (WASE-COVID) Study

ACC 2021: Findings from the WASE COVID Study

Artificial Intelligence Predictors of Death from COVID-19

Left Ventricular Diastolic Function in Healthy Adult Individuals: Results of the World Alliance Societies of Echocardiography Normal Values Study

Echocardiographic Correlates of In-Hospital Death in Patients with Acute COVID-19 Infection: The World Alliance Societies of Echocardiography (WASE-COVID) Study

Human vs AI-Based Echocardiography Analysis as Predictor of Mortality in Acute COVID-19 Patients: WASE-COVID Study

Ultromics Receives FDA Clearance for EchoGo Pro; a First-of-Kind Solution to Diagnose CAD

Anatomic Pathology at the Tipping Point: The Economic Case for Adopting Digital Technology and AI Applications Now

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