Proteomic profiling in cerebrospinal fluid reveal biomarkers for shunt outcome in idiopathic normal-pressure hydrocephalus
- PMID: 40311753
- DOI: 10.1016/j.jare.2025.04.043
Proteomic profiling in cerebrospinal fluid reveal biomarkers for shunt outcome in idiopathic normal-pressure hydrocephalus
Abstract
Background: The pathophysiology of idiopathic normal pressure hydrocephalus (iNPH) remains unclear, and the treatment strategy remains suboptimal. This study aims to identify biomarkers for shunt prognosis by cerebrospinal fluid (CSF) proteomic profiling.
Methods: CSF samples collected from 37 iNPH patients from the discovery cohort and 12 iNPH patients from an independent validation cohort (71.9 ± 6.1 years (mean ± SD)), and 16 age-balanced controls (69.9 ± 7.6 years (mean ± SD)) were collected from September 2020 to December 2023. 53 CSF samples were analyzed using a mass spectrometry-based proteomic workflow. Clinical evaluations were performed on all iNPH patients, and 44 patients underwent ventriculoperitoneal shunting. Postoperative CSF were also collected from 10 iNPH patients who underwent shunting surgery. Bioinformatics, machine learning, and enzyme-linked immunosorbent assay (ELISA) were performed to identify CSF proteome changes related to pathophysiology in iNPH, and screen for biomarkers associated with shunt response.
Results: 39 and 285 proteins significantly increased and decreased in iNPH CSF compared to the control group. Gene ontology analysis revealed that the noticeably increased proteins were mainly associated with myeloid leukocyte migration and extracellular matrix organization, and significantly decreased proteins were primarily associated with axon development and synapse organization. Machine learning identified 6 candidate biomarkers that potentially predicted the response to shunt surgery. Among these, QPCT levels were found to be elevated in non-responders, while RBP4 levels were decreased, and both of these changes were validated through ELISA.
Conclusions: Our findings provide support for the hypothesis that the pathophysiology of iNPH is characterized by a state of neuroinflammation, extracellular matrix remodeling, and neurodegeneration, and CSF shunting can reverse such pathological state. Machine learning using preoperative proteomic profiles satisfactorily predicted the clinical outcome of the shunt procedure in iNPH. Future research targeting specific proteins in iNPH may be warranted to better comprehend the disease mechanism and design patient-tailored treatments.
Keywords: Cerebrospinal fluid; Idiopathic normal pressure hydrocephalus; Machine learning; Proteomics; Shunt surgery response.
Copyright © 2025 The Authors. Published by Elsevier B.V. All rights reserved.
Conflict of interest statement
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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