Association of Blood-Based DNA Methylation Markers With Late-Onset Alzheimer Disease: A Potential Diagnostic Approach

Abstract

Background and objectives: There is an urgent need to identify novel noninvasive biomarkers for Alzheimer disease (AD) diagnosis. Recent advances in blood-based measurements of phosphorylated tau (pTau) species are promising but still insufficient to address clinical needs. Epigenetics has been shown to be helpful to better understand AD pathogenesis. Epigenetic biomarkers have been successfully implemented in other medical disciplines, such as oncology. The objective of this study was to explore the diagnostic accuracy of a blood-based DNA methylation marker panel as a noninvasive tool to identify patients with late-onset Alzheimer compared with age-matched controls.

Methods: A case-control study was performed. Blood DNA methylation levels at 46 cytosine-guanine sites (21 genes selected after a comprehensive literature search) were measured by bisulfite pyrosequencing in patients with "probable AD dementia" following National Institute on Aging and the Alzheimer's Association guidelines (2011) and age-matched and sex-matched controls recruited at Neurology Department-University Hospital of Navarre, Spain, selected by convenience sampling. Plasma pTau181 levels were determined by Simoa technology. Multivariable logistic regression analysis was performed to explore the optimal model to discriminate patients with AD from controls. Furthermore, we performed a stratified analysis by sex.

Results: The final study cohort consisted of 80 patients with AD (age: median [interquartile range] 79 [11] years; 58.8% female) and 100 cognitively healthy controls (age 77 [10] years; 58% female). A panel including DNA methylation levels at NXN, ABCA7, and HOXA3 genes and plasma pTau181 significantly improved (area under the receiver operating characteristic curve 0.93, 95% CI 0.89-0.97) the diagnostic performance of a single pTau181-based model, adjusted for age, sex, and APOE ɛ4 genotype. The sensitivity and specificity of this panel were 83.30% and 90.00%, respectively. After sex-stratified analysis, HOXA3 DNA methylation levels showed consistent association with AD.

Discussion: These results highlight the potential translational value of blood-based DNA methylation biomarkers for noninvasive diagnosis of AD.

Registration information: Research Ethics Committee of the University Hospital of Navarre (PI17/02218).

Figure 1. DNA Methylation Levels in PBLs From AD vs Controls

The panel shows boxplots which represent the percentage of DNA methylation for ABCA7, ADAM10, APOE, BDNF, HOXA3, IRS2, NXN, TREM2, and TREML2 genes in PBLs measured by pyrosequencing. *p < 0.05; **p < 0.01; ***p < 0.001. AD = Alzheimer disease; PBL = peripheral blood leukocyte.

Figure 2. AUC Graph for Each Multivariable Logistic Regression Model

The graphs represent the AUC (95% CI) showing the performance of diagnostic prediction for model 1 (A), model 2 (B), and model 3 (C) for distinguishing AD and controls in the iBEAS cohort. AD = Alzheimer disease; AUC = area under the receiver operating characteristic curve.

Figure 3. Forest Plot Showing Odds Ratios and CIs of Each Variable Predicting Alzheimer Disease

The panel shows odds ratio in logarithmic scale of each variable from each logistic regression for model 1 (A), model 2 (B), and model 3 (C).

Figure 4. Comparisons of Performance Between Multivariable Logistic Regression Model

The graphs represent the AUC (95% CI) showing the performance of diagnostic prediction for model 1, model 2, and model 3 and statistical comparisons of AUCs between the models by the DeLong test after multiple imputation, for distinguishing AD and controls in the iBEAS cohort. *p < 0.05. AD = Alzheimer disease; AUC = area under the receiver operating characteristic curve.