A research team has developed an innovative method for identifying metabolic disorders in newborns, a considerable advancement on traditional methods.
The Baylor College of Medicine researchers have identified that a screening method for identifying metabolic disorders – known as untargeted metabolomics profiling – can significantly improve the diagnostic rate for inborn errors of metabolism by around seven-fold compared to conventional methods.
Their research is published in JAMA Network Open.
Metabolic disorders are a rare group of genetic conditions in which current metabolic screening approaches are not optimal. The novel untargeted metabolomics approach is proficient in distinguishing a higher amount and wider variety of metabolic disorders than traditional methods, even for those with no clinically available biomedical test.
The researchers believe that the implementation of untargeted metabolomics to screen for errors of metabolism will provide a more rapid, efficient, and economical diagnostic journey for patients and families with rare metabolic disorders.
Dr Sarah Elsea, the corresponding author of the study and a professor of molecular and human genetics at Baylor, said: “Currently, newborn screening is conducted in every infant born in the US to check for serious but rare health conditions at birth. Screening includes blood, hearing, and heart tests. While newborn screening, in general, has improved in the last ten years, clinically screening for inborn errors of metabolism has not changed substantially in the last 40 to 50 years.”
Inborn errors of metabolism
Conditions of inborn errors of metabolism disrupt the normal processes in the body that convert food into energy, potentially resulting in severe conditions, with early diagnoses being vital to maximising patient health with early treatment. For example, when screening newborns, medical professionals look for signs of conditions, including phenylketonuria , where the body fails to break down the amino acid phenylalanine, causing it to accumulate. This dangerous build-up can result in damage to the nervous system, with early intervention able to manage the condition.
Elsea said: “We developed a clinical test – untargeted metabolomics profiling – that looks at a broader range of metabolic compounds in the blood, therefore screening for many more disorders than the currently used approach. In the current study, we compared the standard approach and untargeted metabolomics on their effectiveness in identifying metabolic conditions.”
Enhancing identification of metabolic disorders
To test their untargeted metabolomics method, the team applied the technique and the traditional approach to 4,464 clinical samples from 1,483 unrelated families. The results identified that the standard analysis has a positive rate of diagnosis of 1%, whereas the untargeted metabolomics analysis was able to achieve a 7% positive rate of diagnosis.
“This is a substantial increase in the ability to diagnose these conditions,” Elsea said. “We are now able to identify in one blood sample more conditions than ever before.”
Dr V. Reid Sutton, the co-author of the study and a professor of molecular and human genetics at Baylor, said: “In addition, our analysis of many metabolic compounds in a single blood sample reduces the need of having to take more samples to do further testing looking for specific conditions. This includes taking samples of cerebrospinal fluid, which involves a more invasive procedure than drawing a blood sample.”
By combining untargeted metabolomics and genetic screening, researchers and physicians are able to confirm a diagnosis with greater accuracy, additionally ruling out other potential conditions. The innovative technique for identifying metabolic disorders distinguishes severe forms of the disease and also mild forms that don’t conform to the characteristics demonstrated in more severe cases.
“We are finding individuals with milder forms of a disease are more common in our populations than those with severe forms,” Elsea said. “Our approach has been quite successful identifying seizure disorders, movement disorders, and autism spectrum disorders. Our analyses have taught us to open our minds to a much greater spectrum of disease, allowing us to improve early diagnosis.”
