Browsing by Author "Patania, Alice"

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    Characterization of gene expression patterns in mild cognitive impairment using a transcriptomics approach and neuroimaging endophenotypes
    (Wiley, 2022) Bharthur Sanjay, Apoorva; Patania, Alice; Yan, Xiaoran; Svaldi, Diana; Duran, Tugce; Shah, Niraj; Nemes, Sara; Chen, Eric; Apostolova, Liana G.; Neurology, School of Medicine
    Introduction: Identification of novel therapeutics and risk assessment in early stages of Alzheimer's disease (AD) is a crucial aspect of addressing this complex disease. We characterized gene-expression patterns at the mild cognitive impairment (MCI) stage to identify critical mRNA measures and gene clusters associated with AD pathogenesis. Methods: We used a transcriptomics approach, integrating magnetic resonance imaging (MRI) and peripheral blood-based gene expression data using persistent homology (PH) followed by kernel-based clustering. Results: We identified three clusters of genes significantly associated with diagnosis of amnestic MCI. The biological processes associated with each cluster were mitochondrial function, NF-kB signaling, and apoptosis. Cluster-level associations with cortical thickness displayed canonical AD-like patterns. Driver genes from clusters were also validated in an external dataset for prediction of amyloidosis and clinical diagnosis. Discussion: We found a disease-relevant transcriptomic signature sensitive to prodromal AD and identified a subset of potential therapeutic targets associated with AD pathogenesis.
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    Integrating amyloid imaging and genetics for early risk stratification of Alzheimer's disease
    (Wiley, 2024) He, Bing; Wu, Ruiming; Sangani, Neel; Pugalenthi, Pradeep Varathan; Patania, Alice; Risacher, Shannon L.; Nho, Kwangsik; Apostolova, Liana G.; Shen, Li; Saykin, Andrew J.; Yan, Jingwen; Alzheimer’s Disease Neuroimaging Initiative; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Introduction: Alzheimer's disease (AD) initiates years prior to symptoms, underscoring the importance of early detection. While amyloid accumulation starts early, individuals with substantial amyloid burden may remain cognitively normal, implying that amyloid alone is not sufficient for early risk assessment. Methods: Given the genetic susceptibility of AD, a multi-factorial pseudotime approach was proposed to integrate amyloid imaging and genotype data for estimating a risk score. Validation involved association with cognitive decline and survival analysis across risk-stratified groups, focusing on patients with mild cognitive impairment (MCI). Results: Our risk score outperformed amyloid composite standardized uptake value ratio in correlation with cognitive scores. MCI subjects with lower pseudotime risk score showed substantial delayed onset of AD and slower cognitive decline. Moreover, pseudotime risk score demonstrated strong capability in risk stratification within traditionally defined subgroups such as early MCI, apolipoprotein E (APOE) ε4+ MCI, APOE ε4- MCI, and amyloid+ MCI. Discussion: Our risk score holds great potential to improve the precision of early risk assessment. Highlights: Accurate early risk assessment is critical for the success of clinical trials. A new risk score was built from integrating amyloid imaging and genetic data. Our risk score demonstrated improved capability in early risk stratification.