Longitudinal multi-omics profiling of spinal muscular atrophy
Neurotherapeutics, 2026
Dabaj I., Nguyen T., Lagrue E., Ducatez F., Allouche S., Ausseil J., Seferian A., Gomez - Garcia de la Banda M., Benezit A., Phelep A., Chouchane M., Vasseur S., Chapart M., Marret S., Quijano Roy S., Tebani A., Bekri S.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Neurology | Patient Stratification | Plasma CSF | Olink Target 96 |
Abstract
Spinal muscular atrophy (SMA) is an autosomal recessive neuromuscular disorder caused by SMN1 gene variants, leading to the degeneration of anterior horn cells in the spinal cord. It is a disabling disease with varying severity. Nusinersen, the first approved in France, has dramatically transformed SMA management. However, the significant variability in patient response and disease progression highlights a critical need for objective, measurable indicators. This study aims to identify biomarkers in cerebrospinal fluid (CSF) and plasma associated with the clinical status of treatment-naive patients and their disease progression during therapy. We performed targeted metabolomics and proteomics analyses on plasma and CSF samples from SMA patients before and after six months of treatment, along with controls. The differential analysis was carried out to discover the SMA biomarkers. We found that levels of acylcarnitines, biogenic amines, and neurology-related proteins were mainly elevated, while glycerophospholipids primarily decreased in SMA plasma samples compared to controls. The biomarkers showed good performance in distinguishing SMA from controls with plasma AUCs >0.9. NEFH and creatinine were among the most prominent biomarkers for SMA diagnosis. Besides, 26 neurology-related proteins were found to be altered in patient CSF compared to controls. Furthermore, 11 potential proteins were identified to distinguish patients with 2 copies of SMN2 from those with 3 or 4 copies using plasma. By unveiling specific biomarkers, this study offers valuable insights for accurate disease diagnosis and monitoring treatment effectiveness. This enables personalized SMA management and accelerates the development of targeted therapies.