Evaluation of a panel of plasma biomarkers for Alzheimer's disease in a diverse research cohort

Abstract

BackgroundPlasma 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.ObjectiveTo compare plasma biomarkers with clinical diagnoses and assess variability by demographic factors in a diverse research cohort.MethodsAmong 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.ResultsComparing 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.ConclusionsPlasma 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.

Keywords: Alzheimer's disease; GFAP; amyloid-β; blood biomarkers; cognitive dysfunction; dementia; mild cognitive impairment; tau.

Figure 1.

Plasma biomarker levels across clinical phenotype groups. a-f) The levels of (a) pTau-217 (b) pTau-181 (c) glial fibrillary acidic protein (GFAP) (d) Amyloid-β 42 to 40 ratio (Aβ₄₂:Aβ₄₀) (e) neurofilament light chain (NfL) (f) total Tau (t-Tau) and (g) pTau-217: Amyloid-β 42 ratio (pTau-217:Aβ42) were measured in plasma from 161 cognitively unimpaired (CU), 111 mild cognitive impairment (MCI) and 59 Dementia of the Alzheimer type (DAT) participants. Bars are medians and whiskers represent the interquartile ranges. p value is from Dunn’s test: *p < 0.05; **p < 0.01; ***p < 0.001.

Figure 2.

Receiver operating characteristic (ROC) curves for assessed plasma biomarkers. a) Cognitively unimpaired (CU) versus dementia of the Alzheimer’s type (DAT) ROC curves for pTau-217 [navy, AUC = 0.89 (95% CI: 0.84,0.94), p < 0.001], the pTau-217: Amyloid-β 42 ratio [blue, AUC = 0.90 (95% CI: 0.85,0.94), p < 0.001], pTau-181 [teal, AUC = 0.80 (95% CI: 0.74,0.87), p < 0.001], glial fibrillary acidic protein [red, GFAP; AUC = 0.84 (95% CI: 0.77,0.90), p < 0.001], amyloid-β 42 to 40 ratio [orange, Aβ₄₂:Aβ₄₀; AUC = 0.74 (95% CI: 0.67,0.81), p < 0.001], neurofilament light chain [purple, NfL; AUC = 0.78 (95% CI: 0.72,0.85), p < 0.001], and total tau [turquoise, t-Tau; AUC = 0.57 (95% CI: 0.48,0.65), p = 0.129]. b) ROC curves for stepwise regression with backward selection model comparing CU participants and participants diagnosed with DAT [solid; AUC = 0.98 (95% CI: 0.95,1.00)], CU participants and participants diagnosed with MCI [dotted; AUC = 0.67 (95% CI: 0.52,0.82)], and MCI participants and participants diagnosed with DAT [dashed; AUC = 0.90 (95%CI: 0.78,1.00)].

Figure 3.

Self-reported race and levels of plasma biomarkers. a-b) The levels of plasma pTau-217 (a) and pTau-181 (b) were compared between self-identified non-Hispanic white (NHW) and Black/African American (B/AA) participants within the CU (gray), MCI (blue) or DAT (red) clinical phenotype groups. Bars are medians and whiskers represent the interquartile ranges. p values are from Mann-Whitney test comparing NHW to B/AA participants within each clinical phenotype group: *p < 0.05; **p < 0.01; ***p < 0.001.

Figure 4.

Forest plots showing the relationships between self-reported comorbidities [cancer n = 75 (22.7% of the cohort), Depression n = 52 (15.7%), Diabetes n = 48 (14.5%), Hypertension n = 172 (52.0%), Hypercholesterolemia n = 175 (52.9%), Sleep apnea n = 78 (23.6%)] and levels of plasma biomarkers included in our stepwise logistic regression model with backward selection. Coefficients and confidence intervals were calculated for (A) pTau-217, (B) pTau-181, and C) GFAP.