Faculty, Staff and Student Publications

Language

English

Publication Date

6-1-2025

Journal

Nature Aging

DOI

10.1038/s43587-025-00872-8

PMID

40394224

PMCID

PMC12674600

PubMedCentral® Posted Date

12-4-2025

PubMedCentral® Full Text Version

Author MSS

Abstract

Proteomic studies have been instrumental in identifying brain, cerebrospinal fluid and plasma proteins associated with Alzheimer's disease (AD). Here, we comprehensively examined 6,905 aptamers corresponding to 6,106 unique proteins in plasma in more than 3,300 well-characterized individuals to identify new proteins, pathways and predictive models for AD. We identified 416 proteins (294 new) associated with clinical AD status and validated the findings in two external datasets representing more than 7,000 samples. AD-related proteins reflected blood-brain barrier disruption and other processes implicated in AD, such as lipid dysregulation or immune responses. A machine learning model was used to identify a set of seven proteins that were highly predictive of both clinical AD (area under the curve (AUC) of >0.72) and biomarker-defined AD status (AUC of >0.88), which were replicated in multiple external cohorts and orthogonal platforms. These findings underscore the potential of using plasma proteins as biomarkers for the early detection and monitoring of AD and for guiding treatment decisions.

Keywords

Alzheimer Disease, Humans, Biomarkers, Proteomics, Blood Proteins, Male, Female, Aged, Machine Learning, Aged, 80 and over

Published Open-Access

yes

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