Date of Award
2024
Degree Type
Thesis
Degree Name
Master of Science in Interdisciplinary Neurosciences
Department
Biomedical and Pharmaceutical Sciences
First Advisor
Jessica Alber
Abstract
Plasma biomarkers have recently emerged to detect symptomatic Alzheimer Disease (AD), but have yet to be validated in preclinical AD populations, where amyloid beta (Aβ) accumulates in the brain (as measured by amyloid PET scan) but older adults are cognitively unimpaired (CU). In addition to AD pathologic plasma biomarkers (amyloid and tau), inflammatory markers can accurately detect symptomatic AD. We used pathologic and inflammatory plasma biomarkers to predict amyloid PET status in CU older adults.
Participants were 125 CU older adults (mean age = 68) from the Butler Alzheimer’s Prevention Registry who completed amyloid PET through a separate research study. Blood samples were collected and analyzed for the following: an inflammatory panel consisting of 20 proteins, Aβ40, Aβ42, tau (total), p-tau181, and NfL. Multiple regression was used to evaluate the best predictors of amyloid PET status (positive vs. negative) in CU older adults. Model 1 included predictors age, education, and gender. Model 2 and 3 added predictors APOE status, Aβ42/40 ratio and p-tau181 respectively. Random forest (RF) modeling was used to establish the five proteomic markers that best predicted amyloid PET status, and these markers were added in Model 4.
The best model for predicting amyloid PET status included age, years of education, gender, APOE E4 status, Aβ42/40 ratio and p-tau181(p < .01). Adding the top 5 proteomic markers did not significantly improve the model. Results revealed that the proteomic inflammatory markers in plasma did not add predictive value to standard AD pathologic plasma biomarkers in predicting amyloid PET status in CU older adults. This may reflect that the changes associated with inflammatory biomarkers occur later downstream in the pathogenesis and disease progression of AD.
Recommended Citation
Leclerc, Haley, "USING BLOOD BIOMARKERS TO PREDICT PRECLINICAL ALZHEIMER’S DISEASE" (2024). Open Access Master's Theses. Paper 2485.
https://digitalcommons.uri.edu/theses/2485