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Improvements to newborn screening could lower number of false positives

A team of researchers has used an algorithm to improve newborn screening for genetic diseases, with the hopes of reducing the number of false positives.

Imagine you’ve just had a baby, and a doctor delivers alarming news: Screening tests show the baby may have a serious genetic disease called methylmalonic acidemia. To avoid risking brain damage, your baby needs a special, low-protein diet right away. Follow-up testing can confirm the diagnosis.

Between 2005 and 2015, hundreds of California families had these stressful conversations. Yet in most cases — 502 out of 605 — additional testing showed that the babies in question did not have MMA.

The problem, says a new scientific paper published this week in Genetics in Medicine, is that the screening test is very sensitive, meaning it picks up nearly every true case of MMA, but not very specific.

“This approach increases the number of false-positive results, leading to considerable emotional and financial burdens of follow-up testing, unneeded medical precautions for false-positive cases, and diagnostic delays for some infants,” write the paper’s authors, a team from Yale, Stanford and the University of California, San Francisco. Other newborn screening tests also produce many false positives.

Because the diseases in the newborn screening panel are so serious, having sensitive tests is important. In MMA, defective fat and protein metabolism causes rapid buildup of toxic molecules in the blood; quick identification and treatment are lifesaving.

But avoiding false-positive results also has value. The researchers, led by Yale’s Curt Scharfe, MD, PhD, and Stanford’s Tina Cowan, PhD, report on a two-pronged solution.

Current screening tests use dried blood spots collected from newborns. A nurse pricks the baby’s heel and blots blood onto a card, which is sent to a lab. The samples are analyzed with tandem mass spectroscopy, a technique for measuring small molecules in the blood. The tests measure 46 metabolic markers linked with about 40 diseases, including two markers that flag possible MMA cases.

The researchers wondered if they could uncover links between several of the 46 measurements and MMA risk. This type of analysis is difficult for a person but perfect for a machine-learning algorithm. They trained an algorithm to compare three sets of real newborn-screening results: from babies with MMA, babies with false-positive MMA tests, and babies whose screening results were normal.

The computer identified at least six more measurements — from the data already being collected on every baby — that are correlated to MMA diagnosis. Once all of the measurements were considered together, the rate of false positives was cut in half, without reducing the test’s ability to find true cases of MMA.

The team also devised a new method for screening 72 genes linked to metabolic diseases, including MMA. The big challenge here was to produce reliable genetic information from dried blood spots, starting with less than 10 nanograms of patient DNA. Despite the technical challenges, it worked pretty well: The team's genetic testing method picked up 89 percent of true MMA cases.

In combination, the two new methods could spare many families the stress of false-positive test results, while still identifying the infants whose diagnoses need immediate attention, according to the researchers. “Following clinical laboratory validation, [these methods] could be implemented for rapid and inexpensive screening for MMA and other disorders in newborns,” they conclude.

Photo by Dmitri Popov

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