# High-sensitivity plasma proteomics reveals disease-specific signatures and predictive biomarkers of Alzheimer’s disease phenotypes in a large mixed dementia cohort

## TL;DR

This study uses high-sensitivity blood tests to find disease-specific protein markers for Alzheimer's and other dementias, showing promising accuracy for diagnosis and amyloid detection.

## Contribution

The largest plasma proteomic study to date using the NULISA platform, identifying disease-specific biomarkers and validating p-tau217's diagnostic accuracy for Alzheimer's.

## Key findings

- Plasma p-tau217 achieved high accuracy (AUC 0.81) for Alzheimer's diagnosis and strong correlation (AUC 0.95) with amyloid PET status.
- Disease-specific proteins like VEGFD and VEGFA were linked to Alzheimer's, while CCL2 and TREM2 were unique to FTD.
- The NULISA platform demonstrated reliability for detecting biomarkers with high agreement (93.59%) to amyloid PET status.

## Abstract

Highly sensitive plasma assays enable accurate blood-based biomarkers for neurodegenerative disease and provide minimally invasive options for clinical use. Large-scale studies encompassing multiple neurodegenerative diseases and utilizing multiplex platforms are essential to uncover disease-specific biomarkers and pathways.

We generated and analyzed plasma proteomics using the NULISASeq™ CNS Disease Panel 120 from 3,002 participants with Alzheimer disease (AD), Dementia with Lewy bodies (DLB), Frontotemporal dementia (FTD), Parkinson disease (PD) and cognitively unimpaired participants at the Charles F. and Joanne Knight Alzheimer Disease Research Center. We identified proteins associated with disease status and AD-related phenotypes (Clinical Dementia Rating® [CDR®], CSF Aβ42/Aβ40, amyloid-PET, and tau-PET tauopathy), which were used to identify disease-specific biomarkers and perform pathway analyses.

Among the 123 measured protein, 78 were associated with AD, two with DLB, two with FTD, and one with PD after multiple test correction. Disease comparison showed that AD and DLB showed the highest similarity, followed by FTD and DLB. At the same time there were also disease-specific signatures. Some AD-specific proteins include p-tau217 being AD-specific, MME was specific for FTD, CHR for DLB and PARK7 for PD. We also identified 8 proteins associated with Amyloid PET, 7 with Tau PET, 14 with CSF Aβ42/40 ration and 73 with CDR, with Amyloid PET and CDR showing the highest overlap. As few extensive plasma p-tau217 studies have been performed with the NULISA platform, we used a data-driven approach to establish the cut-off for biomarker positivity, and analyze its predictivity performance for clinical status and amyloid-PET. Plasma p-tau217 achieved an AUC of 0.81 (95% CI: 0.79−0.83) for AD diagnosis and 0.95 (95% CI: 0.93−0.98) for amyloid positivity. Using a two-cutoff approach, plasma p-tau217 had an AUC of 0.95 and 93.59% agreement with amyloid-PET status. Proteins associated with AD were enriched on vascular endothelial growth factor receptor binding, mainly driven by VEGFD and VEGFA. Cell death and apoptosis pathways were unique to FTD and driven by CCL2 and TREM2, and PD was enriched on enzymatic activity and metal ion binding.

This is the largest plasma proteomic investigation to date, incorporating p-tau217 and utilizing the NULISA platform to understand neurodegenerative diseases. It validates the high classification accuracy of plasma p-tau217 and its strong correlation with amyloid PET status. Additionally, we identify disease-specific proteins that could enhance differential diagnosis. These findings underscore the potential of the NULISA platform as a reliable quantitative tool for research and clinical applications in neurodegenerative diseases.