Human Gray and White Matter Metabolomics to Differentiate APOE and Stage Dependent Changes in Alzheimer’s Disease

Abstract

Alzheimer's disease (AD) is the most common form of dementia with hallmarks of β-amyloid (Aβ) plaques, tau tangles, and neurodegeneration. Studies have shown that neurodegeneration components, especially brain metabolic deficits, are more predictable for AD severity than Aβ and tau. However, detailed knowledge of the biochemical composition of AD brain tissue vs. normal brain tissue remains unclear. In this study, we performed a metabolomics analysis on the brain tissue of 158 community-based older adults in the University of Kentucky AD Research Center brain bank to characterize the biochemical profiles of brains with and without AD based on white/gray matter type, apolipoprotein E genotype (ε3 vs ε4 variants), and disease stage (early vs late) as all these factors influence metabolic processes. We also used machine learning to rank the top metabolites separating controls and AD in gray and white matter. Compared with control samples, we found that glutamate and creatine metabolism were more critical for predicting AD in the gray matter, while glycine, fatty acid, pyrimidine, tricarboxylic acid (TCA) cycle, and phosphatidylcholine metabolism were more critical in the white matter. In ε4 carriers, metabolites associated with the TCA cycle and oxidative phosphorylation were prominent in advanced stages compared to the early stages. In ε3 carriers, metabolites related to oxidative DNA damage, changes in inhibitory neurotransmitters, and disruptions of neuronal membranes were prominent in advanced stages compared to the early stages. In early disease, ε4 carriers had metabolites related to poor kidney function and altered neuronal sterol metabolism compared to ε3 carriers, but there were few differences between genotypes in late disease. Our results indicate that metabolism plays a pivotal role in differentiating APOE-and stage-dependent changes in AD and may facilitate precision lifestyle and dietary interventions to mitigate AD risk in the early stages, especially for ε4 carriers.

Prebiotic inulin enhances gut microbial metabolism and anti-inflammation in apolipoprotein E4 mice with sex-specific implications

“…Accumulating evidence has shown that neurologically and systemically, metabolism may play a more critical role than amyloid beta plaques and tau tangles in AD progression 52 . In line with the findings in the present study, it has been shown that APOE3 and APOE4 carriers develop AD through different metabolic pathways 72 ; therefore, it will be critical for future studies to identify precision nutrition approaches that tailored to different APOE variants to mitigate AD risk. In our prior work, we have demonstrated that inulin reduces neuroinflammatory gene expression and boosts SCFAs production in APOE4 mice, which mitigates their risk for AD development 12 , 13 .…”

Inulin supplementation prior to mild traumatic brain injury mitigates gut dysbiosis, and brain vascular and white matter deficits in mice

“…It is well documented that TBI increases the risk for developing dementia, such as Alzheimer’s disease (AD) later in life Mortimer et al, 1991 ; Gavett et al, 2010 ; Lee et al, 2013 ; ( Barnes et al, 2016 ). Protecting brain vascular and metabolic functions ( Hammond et al, 2020 ; Hammond et al, 2021 ; Hammond and Lin, 2022 ) is critical to slowing down brain aging ( Hoffman et al, 2017 ) and mitigating the risk for AD ( Lee et al, 2018 ; Lin et al, 2020 ), as these functions serve an essential role in sustaining essential brain activities ( Lin et al, 2008 ; Lin et al, 2010 ). It is also consistent with our previous findings that inulin is protective of systemic metabolism and reduces the risk for AD in a mouse model with human APOE4 alleles, the strongest genetic risk factor for AD ( Hoffman et al, 2019 ; Yanckello et al, 2021 ).…”

Self-Supervised Learning Application on COVID-19 Chest X-ray Image Classification Using Masked AutoEncoder

“…The limitations of our study include the focus on COVID-19 alone. For future work, we plan to extend our work in two directions: first, we will extend the MAE model to handle 3D medical images, such as 3D brain imaging for Alzheimer’s Disease [ 43 , 44 , 45 ]; second, we will explore the potential of the MAE for other tasks, such as image segmentation and localization [ 6 , 46 ], beyond image classification.…”