Blood is routinely drawn from a pinprick of a baby’s foot shortly after birth and investigated for a large group of preventable infections, including in excess of 40 uncommon however possibly debilitating and genuine metabolic disorders. The blood tests are exceptionally sensitive and present a drawback— much of the time they show a disorder when none is present.
Now, Yale scientists have come up with a new approach that they expect to accelerate the diagnosis of diseases in newborns. The approach involves the combination of a new sequencing technique and machine learning.
Curt Scharfe, associate professor of genetics and senior author of the new research said, “These false-positive results can cause great anxiety for parents and prompt a battery of tests before they are revealed. The time until confirmation is stressful for families place a burden on the health care system, and in some cases could delay the right treatment for these infants.”
Through this new approach, scientists can proficiently examine an entire metabolic profile that allows for more precise analysis than previous methods, which focus on a smaller fraction of data collected. The new sequencing method developed by the researchers correctly identified 89% of the newborns with MMA.
It also holds the potential to increase the sensitivity of MMA gene sequencing to that of another newborn DNA test (for cystic fibrosis) they have clinically validated recently.
Scientists noted that the approach could be used to complement existing routine blood work to avoid lengthy testing and speed up the treatment for babies in need of early and additional care.
The Yale team includes the lead authors Gang Peng, a postdoctoral fellow at the Departments of Genetics and Biostatistics at Yale; Neeru Gandotra, an associate research scientist in genetics; and Hongyu Zhao, professor of biostatistics, of genetics, and of statistics and data science.
Scientists have reported their study in the journal Genetics in Medicine.