Artificial intelligence and leukocyte epigenomics: Evaluation and prediction of late-onset Alzheimer’s disease

Abstract

We evaluated the utility of leucocyte epigenomic-biomarkers for Alzheimer’s Disease (AD) detection and elucidates its molecular pathogeneses. Genome-wide DNA methylation analysis was performed using the Infinium MethylationEPIC BeadChip array in 24 late-onset AD (LOAD) and 24 cognitively healthy subjects. Data were analyzed using six Artificial Intelligence (AI) methodologies including Deep Learning (DL) followed by Ingenuity Pathway Analysis (IPA) was used for AD prediction. We identified 152 significantly (FDR p<0.05) differentially methylated intragenic CpGs in 171 distinct genes in AD patients compared to controls. All AI platforms accurately predicted AD with AUCs ≥0.93 using 283,143 intragenic and 244,246 intergenic/extragenic CpGs. DL had an AUC = 0.99 using intragenic CpGs, with both sensitivity and specificity being 97%. High AD prediction was also achieved using intergenic/extragenic CpG sites (DL significance value being AUC = 0.99 with 97% sensitivity and specificity). Epigenetically altered genes included CR1L & CTSV (abnormal morphology of cerebral cortex), S1PR1 (CNS inflammation), and LTB4R (inflammatory response). These genes have been previously linked with AD and dementia. The differentially methylated genes CTSV & PRMT5 (ventricular hypertrophy and dilation) are linked to cardiovascular disease and of interest given the known association between impaired cerebral blood flow, cardiovascular disease, and AD. We report a novel, minimally invasive approach using peripheral blood leucocyte epigenomics, and AI analysis to detect AD and elucidate its pathogenesis.

Precision Oncology: Artificial Intelligence and DNA Methylation Analysis of Circulating Cell-Free DNA for Lung Cancer Detection

“…The array analyzes approximately 850,000 cytosine (‘CpG’ or ‘cg’) loci covering intragenic and extragenic regions of genome. The assay was performed based on the manufacturer’s protocol, as described in detail previously ( 20 ).…”

“…The raw iDat files were analyzed using Illumina GenomeStudio software as described in our earlier studies ( 20 ). The β-values (methylation level at each cytosine locus) were measured and compared for statistical differences between the LC and control groups at each cytosine locus using the genome build hg37.…”

DNA Methylation in Genetic and Sporadic Forms of Neurodegeneration: Lessons from Alzheimer’s, Related Tauopathies and Genetic Tauopathies

“…Age-acceleration has been associated with diminished physical and cognitive fitness [100], and an increase in all-cause mortality [101], but also with a range of age-related diseases, such as AD [102]. Due to this, the epigenetic clock is discussed as a biomarker of aging and age-related disorders, such as AD [103][104][105], as well as of disease progression [106].…”

Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data

“…Such heterogeneity is highly associated with the etiology of the disease, and thus it is important to take such heterogeneity into consideration for each analytical task. Some existing works have already recognized this and focused on a sub-group of AD patients with more homogenized clinical features [136][137][138][139][140]. The existing datasets, such as ADNI and ROSMAP, provided data for relatively large AD sample groups.…”