New screening approach improves diagnosis of metabolic disorders in newborns

A team led by researchers at Baylor College of Medicine has found that a screening method known as untargeted metabolomic profiling can improve the diagnostic rate for inborn errors of metabolism, a group of rare genetic disorders, by approximately seven times compared to traditional metabolic screening. approach.

The study, published in JAMA network open, shows that non-target metabolomics identifies much more troubles more variety compared to traditional methods, including disorders for which there was no clinically available biochemical test. Researchers hope that the adoption of metabolomics to screen inborn errors of metabolism will result in a faster, more efficient and less expensive diagnostic journey for individuals and families with rare metabolic disorders.

“Currently, newborn screening is performed on every infant born in the United States to check for serious but rare health problems at birth. Screening includes blood, hearing and heart tests,” said corresponding author Dr. Sarah Elsea, Professor of Molecular and Human Genetics at Baylor and Senior Director of Biochemical Genetics at Baylor Genetics. “While newborn screening in general has improved over the past 10 years, clinical screening for inborn errors of metabolism has not changed significantly over the past 40 to 50 years.”

Inborn errors of metabolism include conditions that disrupt the normal processes the body uses to turn food into energy and can lead to serious conditions. Early diagnosis can lead to early treatment, when available. For example, newborn screening look for signs of conditions such as phenylketonuria, the body’s inability to break down the amino acid phenylalanine, resulting in its build-up. The buildup of phenylalanine can irreparably damage the nervous system, but early intervention can help manage the condition.

“We have developed a clinical test – untargeted metabolomic profiling – that examines a wider range of metabolic compounds in the blood, thus screening for many more disorders than the approach currently in use,” said Elsea, Dan L Duncan member. Baylor Comprehensive Cancer Center and Center for Drug Discovery. “In the present study, we compared the standard approach and non-target metabolomics on their effectiveness in identifying metabolic conditions.”

The researchers compared the results of applying the two approaches to 4,464 clinical samples received from 1,483 unrelated families. They found that the traditional standard scan has a positive diagnosis rate of around 1%. However, using untargeted metabolomic analysis, the researchers were able to confirm a positive diagnosis rate of 7%.

“This is a substantial increase in the ability to diagnose these conditions,” Elsea said. “We are now able to identify in a single blood sample more conditions than ever before. “

“In addition, our analysis of many metabolic compounds in a single blood sample reduces the need for more samples to perform additional testing for specific conditions. This includes taking cerebrospinal fluid samples, which involves a more invasive procedure than taking a blood sample, ”said co-author Dr. V. Reid Sutton, professor of molecular biology and human genetics to Baylor, medical director of the Biochemical Genetics Laboratory at Baylor Genetics and director of the Inborn Errors of Metabolism department at Texas Children’s Hospital.

This screening approach offers the essential advantages of reducing the time to be diagnosed and to start treatment, if available.

The use of untargeted metabolomics in combination with genetic screening allows researchers and physicians not only to confirm a diagnosis with a high degree of confidence, but also to rule out potential conditions. The novel, larger screening This approach identifies severe forms of disease as well as mild forms that may not quite match the characteristics seen in more severe cases.

“We are finding that people with milder forms of a disease are more common in our populations than those with severe forms,” Elsea said. “Our approach has been quite effective in identifying seizure disorders, movement disorders and autism spectrum disorders. Our analyzes have taught us to open our minds to a much broader spectrum of diseases, allowing us to improve early diagnosis. . ”