Molecular biomarkers for neurodegenerative diseases are critical for advancing diagnosis and therapy. Normal pressure hydrocephalus (NPH) is a neurological disorder characterized by progressive neurodegeneration, gait impairment, urinary incontinence and cognitive decline. In contrast to most other neurodegenerative disorders, NPH symptoms can be improved by the placement of a ventricular shunt that drains excess CSF. A major challenge in NPH management is the identification of patients who benefit from shunt surgery. Here, we perform genome-wide RNA sequencing of extracellular vesicles in CSF of 42 NPH patients, and we identify genes and pathways whose expression levels correlate with gait, urinary or cognitive symptom improvement after shunt surgery. We describe a machine learning algorithm trained on these gene expression profiles to predict shunt surgery response with high accuracy. The transcriptomic signatures we identified may have important implications for improving NPH diagnosis and treatment and for understanding disease aetiology.
Keywords: CSF transcriptomics; machine learning; normal pressure hydrocephalus; shunt surgery response.
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