Plasma biomarkers show significant promise for Alzheimer's disease (AD) diagnostics and risk prediction, however, much less is known about how these assays perform in a diverse research cohort of older adults.
Objective
To compare plasma biomarkers with clinical diagnoses and assess variability by demographic factors in a diverse research cohort.
Methods
Among 331 University of Michigan Memory and Aging Project (UM-MAP) participants, plasma biomarkers (pTau-217, pTau-181, GFAP, NfL, Aβ42, Aβ40, t-Tau) were measured. Demographic information (age, sex, education, race) was self-reported. Clinical consensus phenotypes (dementia of the Alzheimer Type (DAT), mild cognitive impairment (MCI), cognitively unimpaired (CU) were based on neuropsychological assessments. Logistic regression with machine learning for model variable selection was used to compare participants by clinical phenotypes.
Results
Comparing CU and DAT participants, areas under the curve (AUCs) from receiver operator characteristic curves of single biomarker models ranged from 0.74–0.89. Optimal performance (AUC 99.7) was observed from stepwise regression with backward selection, which identified pTau-217, GFAP, sex, education, APOE ε4 allele, and race as model variables. When comparing MCI and DAT participants, only pTau-217 differed significantly (AUC 0.80). pTau-181 and pTau-217 levels were higher in white participants than Black/African American participants across all clinical phenotypes.
Conclusions
Plasma biomarkers demonstrate promise for improving diagnostic accuracy in diverse research cohorts. Incorporating demographic variables facilitates enhanced interpretability of biomarker levels and the development of reference ranges.
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