Cerebrospinal fluid proteomics identification of biomarkers for amyloid and tau PET stages.

Abstract

Accurate staging of Alzheimer's disease (AD) pathology is crucial for therapeutic trials and prognosis, but existing fluid biomarkers lack specificity, especially for assessing tau deposition severity, in amyloid-beta (Aβ)-positive patients. We analyze cerebrospinal fluid (CSF) samples from 136 participants in the Alzheimer's Disease Neuroimaging Initiative using more than 6,000 proteins. We apply machine learning to predict AD pathological stages defined by amyloid and tau positron emission tomography (PET). We identify two distinct protein panels: 16 proteins, including neurofilament heavy chain (NEFH) and SPARC-related modular calcium-binding protein 1 (SMOC1), that distinguished Aβ-negative/tau-negative (A-T-) from A+ individuals and nine proteins, such as HCLS1-associated protein X-1 (HAX1) and glucose-6-phosphate isomerase (GPI), that differentiated A+T+ from A+T- stages. These signatures outperform the established CSF biomarkers (area under the curve [AUC]: 0.92 versus 0.67-0.70) and accurately predicted disease progression over a decade. The findings are validated in both internal and external cohorts. These results underscore the potential of proteomic-based signatures to refine AD diagnostic criteria and improve patient stratification in clinical trials.