Oleg Shchelochkov, MD, NHGRI director of residency and fellowship programs, is also harnessing the power of artificial intelligence to help diagnose rare genetic diseases more accurately.
Specifically, Dr. Shchelochkov is interested in a rare metabolic disorder called propionic acidemia, which affects one in 20,000 to 500,000 people worldwide. Patients with propionic acidemia have higher levels of a chemical called propionic acid in their bodies, which can cause organ damage and frequent hospitalizations. In some cases, a liver transplant is necessary.
For decades, researchers and clinicians have debated the possibility of two types of propionic acidemia—mild and severe—which could affect the type of treatment a patient receives. But because of the limited number of people with this condition, researchers find it difficult to predict which patients might benefit from the different treatment approaches.
Recently, Dr. Shchelochkov published a study with Charles Venditti, MD, Ph.D., head of the NHGRI Metabolic Medicine Branch, which used machine learning to find biological markers, also called biomarkers, associated with mild and severe forms of the condition.
The researchers collected nearly 500 types of genetic, laboratory and imaging data. After working with propionic acidemia disease experts to create a system for classifying patients into mild and severe categories, the researchers trained the algorithm to determine which pieces of data are uniquely associated with the two forms of the disease. After training, the researchers gave the algorithm new information about a patient. The algorithm was very successful in establishing which data types were associated with the mild versus severe form of propionic acidemia.
If we can use machine learning to make these kinds of useful predictions about rare diseases, even with so little data, that would be a boon for more common conditions like cancer, hypertension, and diabetes.
The results of this study support a decades-long intuition held by experienced clinicians that there are distinct versions of propionic acidemia. With early insights into the severity of a given case, clinicians can better design the treatment plan for that patient.
“It would be very difficult for people to distill that much data into what really matters for the severity of the disorder,” says Dr. Shchelochkov. “This is the kind of predictive power we want to continue to leverage for future endeavors.”
With information about which biomarkers are most closely related to the severity of propionic acidemia, clinicians can focus on identifying severe patients more quickly and providing them with the help they need as soon as possible.
“If we can use machine learning to make these kinds of useful predictions about rare diseases, even with so little data, it would be a boon for more common conditions like cancer, hypertension and diabetes,” says Dr. Venditti.
