Browsing by Author "Idler, Kenneth"

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    Harnessing peripheral DNA methylation differences in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to reveal novel biomarkers of disease
    (Springer, 2020-06-15) Vasanthakumar, Aparna; Davis, Justin W.; Idler, Kenneth; Waring, Jeffrey F.; Asque, Elizabeth; Riley-Gillis, Bridget; Grosskurth, Shaun; Srivastava, Gyan; Kim, Sungeun; Nho, Kwangsik; Nudelman, Kelly N. H.; Faber, Kelley; Sun, Yu; Foroud, Tatiana M.; Estrada, Karol; Apostolova, Liana G.; Li, Qingqin S.; Saykin, Andrew J.; for the Alzheimer’s Disease Neuroimaging Initiative (ADNI); Radiology and Imaging Sciences, School of Medicine
    Background Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease impacting an estimated 44 million adults worldwide. The causal pathology of AD (accumulation of amyloid-beta and tau), precedes hallmark symptoms of dementia by more than a decade, necessitating development of early diagnostic markers of disease onset, particularly for new drugs that aim to modify disease processes. To evaluate differentially methylated positions (DMPs) as novel blood-based biomarkers of AD, we used a subset of 653 individuals with peripheral blood (PB) samples in the Alzheimer’s disease Neuroimaging Initiative (ADNI) consortium. The selected cohort of AD, mild cognitive impairment (MCI), and age-matched healthy controls (CN) all had imaging, genetics, transcriptomics, cerebrospinal protein markers, and comprehensive clinical records, providing a rich resource of concurrent multi-omics and phenotypic information on a well-phenotyped subset of ADNI participants. Results In this manuscript, we report cross-diagnosis differential peripheral DNA methylation in a cohort of AD, MCI, and age-matched CN individuals with longitudinal DNA methylation measurements. Epigenome-wide association studies (EWAS) were performed using a mixed model with repeated measures over time with a P value cutoff of 1 × 10−5 to test contrasts of pairwise differential peripheral methylation in AD vs CN, AD vs MCI, and MCI vs CN. The most highly significant differentially methylated loci also tracked with Mini Mental State Examination (MMSE) scores. Differentially methylated loci were enriched near brain and neurodegeneration-related genes (e.g., BDNF, BIN1, APOC1) validated using the genotype tissue expression project portal (GTex). Conclusions Our work shows that peripheral differential methylation between age-matched subjects with AD relative to healthy controls will provide opportunities to further investigate and validate differential methylation as a surrogate of disease. Given the inaccessibility of brain tissue, the PB-associated methylation marks may help identify the stage of disease and progression phenotype, information that would be central to bringing forward successful drugs for AD.
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    Integrative analysis of DNA methylation and gene expression identifies genes associated with biological aging in Alzheimer's disease
    (Wiley, 2022-09-20) Kim, Bo-Hyun; Vasanthakumar, Aparna; Li, Qingqin S.; Nudelman, Kelly N.H.; Risacher, Shannon L.; Davis, Justin W.; Idler, Kenneth; Lee, Jong-Min; Seo, Sang Won; Waring, Jeffrey F.; Saykin, Andrew J.; Nho, Kwangsik; Alzheimer’s Disease Neuroimaging Initiative (ADNI); Radiology and Imaging Sciences, School of Medicine
    Introduction: The acceleration of biological aging is a risk factor for Alzheimer's disease (AD). Here, we performed weighted gene co-expression network analysis (WGCNA) to identify modules and dysregulated genes involved in biological aging in AD. Methods: We performed WGCNA to identify modules associated with biological clocks and hub genes of the module with the highest module significance. In addition, we performed differential expression analysis and association analysis with AD biomarkers. Results: WGCNA identified five modules associated with biological clocks, with the module designated as "purple" showing the strongest association. Functional enrichment analysis revealed that the purple module was related to cell migration and death. Ten genes were identified as hub genes in purple modules, of which CX3CR1 was downregulated in AD and low levels of CX3CR1 expression were associated with AD biomarkers. Conclusion: Network analysis identified genes associated with biological clocks, which suggests the genetic architecture underlying biological aging in AD. Highlights: Examine links between Alzheimer's disease (AD) peripheral transcriptome and biological aging changes. Weighted gene co-expression network analysis (WGCNA) found five modules related to biological aging. Among the hub genes of the module, CX3CR1 was downregulated in AD. The CX3CR1 expression level was associated with cognitive performance and brain atrophy.