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Abstract INTRODUCTIONAlzheimer'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. METHODSGiven 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). RESULTSOur 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,APOEε4– MCI, and amyloid+ MCI. DISCUSSIONOur risk score holds great potential to improve the precision of early risk assessment. HighlightsAccurate 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. 

Abstract BackgroundUsing remote assessments for Alzheimer's Disease and Related Disorders (ADRD) studies can have advantages, such as providing research opportunities to individuals who might otherwise be excluded due to geographical distances, transportation difficulties, and physical frailty. As studies adopt remote assessment modalities, however, people at highest risk of ADRD may be less likely to use the internet, own electronic devices, and be comfortable with technology utilization. MethodThese analyses included data obtained through the National Alzheimer's Coordinating Center Uniform Data Set from 3,803 participants across 17 Alzheimer's Disease Research Centers in the United States who completed the Technology Access Survey between July 2^nd, 2020 and April 26^th, 2023. Participants were categorized as either White or Other Race or Ethnicity. Mixed effects logistic regression models using generalized estimating equations with random effect for study site were used to examine the association of education, race and ethnicity, and education x Other Race or Ethnicity interaction with (1) device use and (2) device preferences for remote assessments. The analyses were adjusted for age, sex, cognitive status, and study site. Significance was set atp <0.05. ResultDescriptive statistics are shown in Table 1. Participants with more years of education had greater access to the internet across all devices (Table 2). Other Race or Ethnicity participants had lower odds of access to tablet, laptop and desktop computer compared to White participants. There was a significant interaction between Other Race or Ethnicity and education for use of tablet, laptop and desktop computer, where the effect of higher education was greater in Other Race or Ethnicity than White participants. A similar pattern of results was observed for interest in using a smartphone, tablet, laptop or desktop computer to complete parts of their study visit from home (Table 3). ConclusionThese findings suggest that education has a role in racioethnic differences in technological access and preferences. Future ADRD studies utilizing remote assessments should consider these patterns to inform study design and potential selection of populations studied. 

Background: There are various molecular hypotheses regarding Alzheimer’s disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addition, genetic contribution of these molecular hypothesis is not yet established despite the high heritability of AD. Objective: The study aims to enable the discovery of functionally connected multi-omic features through novel integration of multi-omic data and prior functional interactions. Methods: We propose a new deep learning model MoFNet with improved interpretability to investigate the AD molecular mechanism and its upstream genetic contributors. MoFNet integrates multi-omic data with prior functional interactions between SNPs, genes, and proteins, and for the first time models the dynamic information flow from DNA to RNA and proteins. Results: When evaluated using the ROS/MAP cohort, MoFNet outperformed other competing methods in prediction performance. It identified SNPs, genes, and proteins with significantly more prior functional interactions, resulting in three multi-omic subnetworks. SNP-gene pairs identified by MoFNet were mostly eQTLs specific to frontal cortex tissue where gene/protein data was collected. These molecular subnetworks are enriched in innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. We validated most findings in an independent dataset. One multi-omic subnetwork consists exclusively of core members of SNARE complex, a key mediator of synaptic vesicle fusion and neurotransmitter transportation. Conclusions: Our results suggest that MoFNet is effective in improving classification accuracy and in identifying multi-omic markers for AD with improved interpretability. Multi-omic subnetworks identified by MoFNet provided insights of AD molecular mechanism with improved details.