Recent array-based epigenome-wide association studies (EWAS) of brain tissue report differential DNA methylation in known and newly recognized late-onset sporadic Alzheimer?s disease (LOAD) genes, thereby underscoring the utility of EWAS in disclosing novel genes and pathways associated with LOAD pathogenesis. As an alternative to the study of samples from donor brains, investigation of DNA methylation in accessible peripheral tissues offers the opportunity to improve LOAD diagnosis and prognosis. Recently, we?ve identified differentially methylated positions (DMPs) in blood that distinguish men and women with and without LOAD at 477 of 769,190 loci in a plurality of genes. Of these DMPs, 17 are shared between DMPs observed using clinical LOAD markers analyzed independently as continuous variables comprising Rey Auditory Verbal Learning Test scores, cerebrospinal fluid total tau (t-tau) and phosphorylated tau 181 (p-tau181) levels, and t-tau/A?1?42 (A?42), p-tau181/A?42, and A?42/A?1?40 (A?40) ratios. In patients with LOAD, 12 of the shared 17 DMPs are hypo- methylated in B3GALT4 (Beta-1,3-galatcosyltransferase 4), a gene previously associated with LOAD in superior temporal gyrus brain tissue, and 5 are hypo-methylated in ZADH2 (Prostaglandin reductase 3), a novel LOAD- associated gene. Together these data reinforce use of blood to identify DMPs associated with dementia that arises from LOAD, leading to the hypothesis that DNA methylation levels in blood may be used to identify novel diagnostic, prognostic, and modifiable therapeutic targets of LOAD. Using a whole-genome-based approach, this proposal builds upon the Wisconsin Alzheimer?s Disease Research Center?s (WADRC) existing banked biofluids and phenotypic data to validate the 477 DMPs, including sites in B3GALT4 and ZADH2, as biomarkers of LOAD, while at the same time examining DNA methylation levels across the entire human genome (>25 million loci), with the potential to further identify novel DNA methylation predictors of LOAD. In addition, these studies will be expanded by examining a second cohort of female and male participants presently enrolled in the WADRC with and without mild cognitive impairment (MCI) to identify DNA methylation biomarkers prior to the onset of LOAD. MCI is an intermediate stage between cognitively normal older adults and LOAD. Patients with MCI have an elevated risk of progressing to dementia. Eighty percent of MCI patients convert to LOAD after an average of 6 years. Blood biomarkers that distinguish patients with MCI who later progress to LOAD from those with MCI who do not progress to LOAD offer a substantial opportunity to improve the diagnosis and early intervention in patients with accelerated cognitive aging. Together, findings from the present proposal will provide the foundation for identifying DNA methylation profiles in blood that predict the expression and progression to LOAD, detect deviations from healthy cognitive trajectories, identify modifiable risk factors and interventions, and bolster research efforts with an epigenetic metric that integrates heritable and acquired variables that influence aging.
The proposed research is extraordinarily relevant to public health. Our human data will provide the foundation for identifying DNA methylation profiles in blood that predict the progression and expression of late onset Alzheimer?s disease (LOAD). Conventional biomarkers for Alzheimer?s disease are costly, invasive, and must be performed in select locations by specialists, thereby reducing their utility for population-wide diagnosis and prognosis. Accordingly, the present proposal expressly addresses NIA?s mission with the discovery and validation of reliable and practical Alzheimer?s disease blood biomarkers. Blood biomarkers configured for use in primary-care settings will detect deviations from healthy cognitive trajectories over the lifespan, identify modifiable Alzheimer?s disease risk factors and interventions, and coordinate research efforts with an epigenetic metric that integrates heritable and acquired variables that influence aging.
