Pilot study of genome‐wide differences in DNA methylation among older adults with normal cognition and mild cognitive impairment, with and without neuropsychiatric symptoms

Background Genome-wide DNA methylation assay (DNAm) reflects epigenetic aging and differences in gene expression, and may help to elucidate the role of systemic aging in cognitive decline. Neuropsychiatric symptoms (NPS) are common in mild cognitive impairment (MCI) and may increase the risk of progression to dementia. However, few investigations have addressed whether genome-wide differences in DNA methylation underlie presentations of NPS in MCI. Method We included 45 older participants (mean age=70 years) from a depression prevention ancillary study (VITAL-DEP) to the VITamin D and OmegA-3 TriaL. We defined 3 groups: cognitively normal (CN; n=20); amnestic MCI (aMCI; n=16); non-amnestic MCI (naMCI; n=9). Genomic DNA was extracted from leukocytes; DNA methylation was assayed using Illumina MethylationEPIC (Methyl850K chip). DNAm age metrics were computed using Horvath methods; genome-wide differential methylation was examined among the 866,836 CpG sites in MethylationEPIC. Annotations were performed using Gene Ontology (GO) databases and tools. Result The sample was balanced by age and sex across cognitive groups (CN and MCI). Participants with MCI had higher DNAmAge compared to those with CN [median (interquartile range): 66.4 (63.5-71.1) vs. 65.3 (63.4-69.2)]. Comparing MCI vs. CN, there were 48 CpG sites at p<1x10-4, but not genome-wide-significant, suggesting possible differential methylation in novel genes. For example, over-representation analysis revealed genes associated with dysregulation of the tight junction (ACTR3, TUBA4A, JUN) and one-carbon folate pathways (DPYD, GART), suggesting blood-brain barrier dysfunction and metabolic dysregulation. Comparing MCI with vs. without NPS, there were 49 CpG sites at p<1x10-4, but not genome-wide significant. Top hits in GO suggested involvement of negative regulation of neuron projection development (KANK1, RAP1GAP2), activation of protein kinase C activity (PARD3, F2R), regulation of blood coagulation (STXBP5, F2R), and glycosaminoglycan degradation (HSPG2, HGSNAT). Functional annotations suggested involvement of important cellular pathways (e.g., serotonergic synapse, alpha-linolenic metabolism, calcium signaling). Further, several identified genes had prior reports as summarized in the AlzGene database (e.g., PPP1R10, HSPG2). Conclusion Genome-wide differential methylation in DNAm analysis suggested potential genomic variations in MCI vs. CN and in NPS among patients with MCI. If confirmed, such genomic biomarkers could inform development of targets in prevention studies.