A dual proteomics analysis of paired cerebrospinal fluid and plasma from patients with neurodegenerative diseases
Year: 2026
Type: preprint
Study design: Observational Study
Abstract: INTRODUCTION Understanding concordance across biofluids and platforms is critical for understanding neurodegenerative biomarkers results and translating them into clinical use; yet systematic comparisons remain limited. To address this gap, we performed large-scale proteomic profiling of patient-paired plasma and CSF to characterize cross-modal relationships. METHODS We profiled paired plasma and CSF from 67 individuals using SomaScan 11K and NULISAseq CNS panels. Disease severity was assessed with the CDR+NACC FTLD-M Global Score. RESULTS We identified 269 SomaScan and 18 NULISA proteins with significant cross-biofluid correlation. Cross-platform concordance within biofluids was strong. NEFL, NPTX2, TREM2, and CHIT1 demonstrated consistent cross-platform agreement. Associations with disease severity were compartment-specific, with decreased NPTX2 in CSF, increased NEFL and GFAP in plasma, and decreased TREM2 across biofluids. DISCUSSION Cross-platform consistency supports biomarker robustness, while limited cross-biofluid concordance highlights compartmental biology. Given additional clinical correlations despite varying underlying pathology, these findings may point to shared neurodegenerative disorder pathways.
Source: medRxiv
Authors: Isabelle Kowal, Sonja W. Scholz, Jacob Epstein, Bryan J. Traynor, Laura E. Danielian, Ying Hao, Jody Crook, Ziyi Li, Katelyn Porter, Brian Sellers, Thomas J. Langowski, Marisa N. Denkinger, Nicholas J. Ashton, Kendall Van Keuren-Jensen, Mark R. Cookson, Justin Y. Kwan, Yue A. Qi, Allison Snyder
Institutions: National Institutes of Health, National Institute of Neurological Disorders and Stroke, National Institute on Aging, Johns Hopkins University, Johns Hopkins Hospital, Banner Sun Health Research Institute, University of Gothenburg, Translational Genomics Research Institute, University of Pennsylvania
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7211903942