Metabolic Urine Analysis May Help Diagnose, Predict SMA

A new study suggests clinicians might be able to use metabolic “fingerprints” to better understand the likely severity of individual cases.

A new report suggests metabolic profiling may be able to give clinicians a much clearer picture of individual cases of 5q spinal muscular atrophy (SMA), creating the potential for earlier, more targeted therapeutic intervention.

The study was published in the Orphanet Journal of Rare Diseases.

SMA is caused by loss of function or deletion of the SMN1 gene with retained function of at least one copy of the SMN2 gene, explained corresponding author Andreas Ziegler, M.D., of Heidelberg University Hospital, in Germany, and colleagues.

New advances in the treatment of SMA have made it possible to significantly improve the trajectories of cases, particularly when children are diagnosed early. But newest therapies also come at a high cost and can be burdensome to administer.

Ziegler told Managed Healthcare Executive® that the current status quo involves making treatment decisions based primarily on the quantity of SMN2 copy numbers. However, that method is imprecise.

“The biggest problem currently is to determine the optimal ‘window of opportunity’ for the different SMA treatments,” Ziegler said.

While gene therapy can have a meaningful impact on patients with SMA, investigators do not yet know if the benefits of the therapy are permanent.

“That means treating these children too early in a presymptomatic stage of the disease might be an over-treatment at their current stage of disease, making a later gene therapy impossible when they really need it (that means: one extreme is too early treatment),” he said.

In cases where an infant screening identifies a newborn as having a type of SMA likely to have a later-in-childhood onset, clinicians have to make a difficult choice about when to actually start therapy.

“For these children we have also to focus on possible toxic effects of ‘too much SMA treatment’ with the result of toxic high levels of the SMN-protein,” he said. “Sometimes it would probably be less harmful to watch and wait and monitor the children closely.”

Noting that urinary metabolic profiling has successfully been used in other disorders of the central nervous system, Ziegler and colleagues set about to investigate whether the use of 1H-nuclear magnetic resonance (NMR)-based metabolomics might be able to identify reliable biomarkers of SMA diagnosis and disease prediction.

The investigators collected urine samples from 29 patients with SMA, all of whom were treatment naive. Of those 29, 5 were presymptomatic, and the remaining patients were split among the 3 types of SMA that appear in childhood. Samples were also collected from 18 patients with Duchenne muscular dystrophy and from 444 healthy controls.

The authors then used machine learning to analyze the samples to see if patterns could be identified that might match with phenotypic severity.

The analysis suggested a “metabolic fingerprint” that the investigators said might allow clinicians to distinguish children with SMA from healthy controls and from children with Duchenne muscular dystrophy. However, those “fingerprints” appear to be able to distinguish beyond a blanket SMA diagnosis.

“Importantly, profiles of affected SMA patients, in the majority of cases, displayed some correlation to phenotypic severity and might be able to reflect dynamic changes under therapy,” Ziegler and colleagues said.

They added that the analysis suggests that multiple regions of the metabolome appeared useful in stratifying patients, “emphasizing the concept of complex and multifactorial biochemical changes driving SMA phenotypes, as opposed to a single-biomarker-based approach to predict disease severity.”

The authors cautioned that they are still investigating these metabolic profiles, and in any case, the findings would need to be refined and tested in larger groups.

Still, they said, this early proof-of-concept study suggests it may soon be possible to use a non-invasive method to diagnose and classify SMA.

Ziegler told MHE that he envisions the new screening as a second-tier approach, following genetic testing.

“After having the first positive tier with a suspicion of SMA, NMR might be used as a second tier to determine the biochemical onset of the disease and stratify for SMA therapies,” he said.

In addition, he said the technology could be used as a monitoring tool to track the effectiveness of particular therapies in particular patients, though he said this is still a work in progress.