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4 Evaluating Plasma GFAP for the Detection of Alzheimer’s Disease Dementia

Published online by Cambridge University Press:  21 December 2023

Madeline Ally*
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Henrik Zetterberg
Affiliation:
University of Gothenburg, Mölndal, Sweden.
Kaj Blennow
Affiliation:
University of Gothenburg, Mölndal, Sweden.
Nicholas J. Ashton
Affiliation:
University of Gothenburg, Mölndal, Sweden.
Thomas K. Karikari
Affiliation:
University of Gothenburg, Mölndal, Sweden.
Hugo Aparicio
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Michael A. Sugarman
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Brandon Frank
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Yorghos Tripodis
Affiliation:
Boston University School of Public Health, Boston, MA, USA
Ann C. McKee
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Thor D. Stein
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Brett Martin
Affiliation:
Boston University School of Public Health, Boston, MA, USA
Joseph N. Palmisano
Affiliation:
Boston University School of Public Health, Boston, MA, USA
Eric G. Steinberg
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Irene Simkina
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Lindsay Farrer
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Gyungah Jun
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Katherine W. Turk
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Andrew E. Budson
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Maureen K. O’Connor
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Rhoda Au
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Wei Qiao Qiu
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Lee E. Goldstein
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Ronald Killiany
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Neil W. Kowall
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Robert A. Stern
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Jesse Mez
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
Michael L. Alosco
Affiliation:
Boston University School of Medicine, Boston, MA, USA.
*
Correspondence: Madeline Ally, Boston University School of Medicine, mally@bu.edu.
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Abstract

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Objective:

Blood-based biomarkers represent a scalable and accessible approach for the detection and monitoring of Alzheimer’s disease (AD). Plasma phosphorylated tau (p-tau) and neurofilament light (NfL) are validated biomarkers for the detection of tau and neurodegenerative brain changes in AD, respectively. There is now emphasis to expand beyond these markers to detect and provide insight into the pathophysiological processes of AD. To this end, a reactive astrocytic marker, namely plasma glial fibrillary acidic protein (GFAP), has been of interest. Yet, little is known about the relationship between plasma GFAP and AD. Here, we examined the association between plasma GFAP, diagnostic status, and neuropsychological test performance. Diagnostic accuracy of plasma GFAP was compared with plasma measures of p-tau181 and NfL.

Participants and Methods:

This sample included 567 participants from the Boston University (BU) Alzheimer’s Disease Research Center (ADRC) Longitudinal Clinical Core Registry, including individuals with normal cognition (n=234), mild cognitive impairment (MCI) (n=180), and AD dementia (n=153). The sample included all participants who had a blood draw. Participants completed a comprehensive neuropsychological battery (sample sizes across tests varied due to missingness). Diagnoses were adjudicated during multidisciplinary diagnostic consensus conferences. Plasma samples were analyzed using the Simoa platform. Binary logistic regression analyses tested the association between GFAP levels and diagnostic status (i.e., cognitively impaired due to AD versus unimpaired), controlling for age, sex, race, education, and APOE e4 status. Area under the curve (AUC) statistics from receiver operating characteristics (ROC) using predicted probabilities from binary logistic regression examined the ability of plasma GFAP to discriminate diagnostic groups compared with plasma p-tau181 and NfL. Linear regression models tested the association between plasma GFAP and neuropsychological test performance, accounting for the above covariates.

Results:

The mean (SD) age of the sample was 74.34 (7.54), 319 (56.3%) were female, 75 (13.2%) were Black, and 223 (39.3%) were APOE e4 carriers. Higher GFAP concentrations were associated with increased odds for having cognitive impairment (GFAP z-score transformed: OR=2.233, 95% CI [1.609, 3.099], p<0.001; non-z-transformed: OR=1.004, 95% CI [1.002, 1.006], p<0.001). ROC analyses, comprising of GFAP and the above covariates, showed plasma GFAP discriminated the cognitively impaired from unimpaired (AUC=0.75) and was similar, but slightly superior, to plasma p-tau181 (AUC=0.74) and plasma NfL (AUC=0.74). A joint panel of the plasma markers had greatest discrimination accuracy (AUC=0.76). Linear regression analyses showed that higher GFAP levels were associated with worse performance on neuropsychological tests assessing global cognition, attention, executive functioning, episodic memory, and language abilities (ps<0.001) as well as higher CDR Sum of Boxes (p<0.001).

Conclusions:

Higher plasma GFAP levels differentiated participants with cognitive impairment from those with normal cognition and were associated with worse performance on all neuropsychological tests assessed. GFAP had similar accuracy in detecting those with cognitive impairment compared with p-tau181 and NfL, however, a panel of all three biomarkers was optimal. These results support the utility of plasma GFAP in AD detection and suggest the pathological processes it represents might play an integral role in the pathogenesis of AD.

Type
Poster Session 04: Aging | MCI
Copyright
Copyright © INS. Published by Cambridge University Press, 2023