Abstract
INTRODUCTION
Plasma proteomic analyses of unique brain atrophy patterns may illuminate peripheral drivers of neurodegeneration and identify novel biomarkers for predicting clinically relevant outcomes.
METHODS
We identified proteomic signatures associated with machine learning-derived aging- and Alzheimer’s disease (AD) -related brain atrophy patterns in the Baltimore Longitudinal Study of Aging (n = 815). Using data from five cohorts, we examined whether candidate proteins were associated with AD endophenotypes and long-term dementia risk.
RESULTS
Plasma proteins associated with distinct patterns of age- and AD-related atrophy were also associated with plasma/cerebrospinal fluid (CSF) AD biomarkers, cognition, AD risk, as well as mid-life (20-year) and late-life (8-year) dementia risk. EFEMP1 and CXCL12 showed the most consistent associations across cohorts and were mechanistically implicated as determinants of brain structure using genetic methods, including Mendelian randomization.
DISCUSSION
Our findings reveal plasma proteomic signatures of unique aging- and AD-related brain atrophy patterns and implicate EFEMP1 and CXCL12 as important molecular drivers of neurodegeneration.
Highlights
Plasma proteomic signatures are associated with unique patterns of brain atrophy.
Brain atrophy-related proteins predict clinically relevant outcomes across cohorts.
Genetic variation underlying plasma EFEMP1 and CXCL12 influences brain structure.
EFEMP1 and CXCL12 may be important molecular drivers of neurodegeneration.
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This post is Copyright: Michael R. Duggan,
Zhijian Yang,
Yuhan Cui,
Heather E. Dark,
Junhao Wen,
Guray Erus,
Timothy J. Hohman,
Jingsha Chen,
Alexandria Lewis,
Abhay Moghekar,
Josef Coresh,
Susan M. Resnick,
Christos Davatzikos,
Keenan A. Walker | August 12, 2024