Dataset Information

Divergence in the metabolome between natural aging and Alzheimer's disease.

ABSTRACT: Alzheimer's disease (AD) is a progressive and debilitating neurodegenerative disorder and one of the leading causes of death in the United States. Although amyloid plaques and fibrillary tangles are hallmarks of AD, research suggests that pathology associated with AD often begins 20 or more years before symptoms appear. Therefore, it is essential to identify early-stage biomarkers in those at risk for AD and age-related cognitive decline (ARCD) in order to develop preventative treatments. Here, we used an untargeted metabolomics analysis to define system-level alterations following cognitive decline in aged and APP/PS1 (AD) mice. At 6, 12, and 24 months of age, both control (Ctrl) and AD mice were tested in a 3-shock contextual fear conditioning (CFC) paradigm to assess memory decline. AD mice exhibited memory deficits across age and these memory deficits were also seen in naturally aged mice. Prefrontal cortex (PFC), hippocampus (HPC), and spleen were then collected and analyzed for metabolomic alterations. A number of significant pathways were altered between Ctrl and AD mice and naturally aged mice. By identifying systems-level alterations following ARCD and AD, these data could provide insights into disease mechanisms and advance the development of biomarker panels.

SUBMITTER: Hunsberger HC

PROVIDER: S-EPMC7376199 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Divergence in the metabolome between natural aging and Alzheimer's disease.

Hunsberger Holly C HC Greenwood Bennett P BP Tolstikov Vladimir V Narain Niven R NR Kiebish Michael A MA Denny Christine Ann CA

Scientific reports 20200722 1

Alzheimer's disease (AD) is a progressive and debilitating neurodegenerative disorder and one of the leading causes of death in the United States. Although amyloid plaques and fibrillary tangles are hallmarks of AD, research suggests that pathology associated with AD often begins 20 or more years before symptoms appear. Therefore, it is essential to identify early-stage biomarkers in those at risk for AD and age-related cognitive decline (ARCD) in order to develop preventative treatments. Here, ...[more]

PMID: 32699218

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Project description:Alzheimer disease is one of the most challenging demons in our society due to its very high prevalence and its clinical manifestations which cause deterioration of cognition, intelligence, and emotions - the very capacities that distinguish Homo sapiens from other animal species. Besides the personal, social, and economical costs, late stages of AD are vivid experiences for the family, relatives, friends, and general observers of the progressive ruin of an individual who turns into a being with lower mental and physical capacities than less evolved species. A human brain with healthy cognition, conscience, and emotions can succeed in dealing with most difficulties that life may pose. Without these capacities, the same person probably cannot. Due, in part, to this emotional impact, the absorbing study of AD has generated, over the years, a fascinating and complex story of theories, hypotheses, controversies, fashion swings, and passionate clashes, together with tremendous efforts and achievements geared to improve understanding of the pathogenesis and treatment of the disorder. Familal AD is rare and linked to altered genetic information associated with three genes. Sporadic AD (sAD) is much more common and multifactorial. A major point of clinical discussion has been, and still is, establishing the differences between brain aging and sAD. This is not a trivial question, as the neuropathological and molecular characteristics of normal brain aging and the first appearance of early stages of sAD-related pathology are not easily distinguishable in most individuals. Another important point is confidence in assigning responsibility for the beginning of sAD to a few triggering molecules, without considering the wide number of alterations that converge in the pathogenesis of aging and sAD. Genetic risk factors covering multiple molecular signals are increasing in number. In the same line, molecular pathways are altered at early stages of sAD pathology, currently grouped under the aegis of normal brain aging, only to increase massively at advanced stages of the process. Sporadic AD is here considered an inherent, natural part of human brain aging, which is prevalent in all humans, and variably present or not in a few individuals in other species. The progression of the process has devastating effects in a relatively low percentage of human beings eventually evolving to dementia. The continuum of brain aging and sAD implies the search for a different approach in the study of human brain aging at the first stages of the biological process, and advances in the use of new technologies aimed at slowing down the molecular defects underlying human brain aging and sAD at the outset, and transfering information and tasks to AI and coordinated devices.

Project description:Alzheimer's disease (AD) is the leading cause of dementia; however, men and women face differential AD prevalence, presentation, and progression risks. Characterizing metabolomic profiles during AD progression is fundamental to understand the metabolic disruptions and the biological pathways involved. However, outstanding questions remain of whether peripheral metabolic changes occur equally in men and women with AD. Here, we evaluated differential effects of metabolomic and brain volume associations between sexes. We used three cohorts from the Alzheimer's Disease Neuroimaging Initiative (ADNI), evaluated 1,368 participants, two metabolomic platforms with 380 metabolites in total, and six brain segment volumes. Using dimension reduction techniques, we took advantage of the correlation structure of the brain volume phenotypes and the metabolite concentration values to reduce the number of tests while aggregating relevant biological structures. Using WGCNA, we aggregated modules of highly co-expressed metabolites. On the other hand, we used partial least squares regression-discriminant analysis (PLS-DA) to extract components of brain volumes that maximally co-vary with AD diagnosis as phenotypes. We tested for differences in effect sizes between sexes in the association between single metabolite and metabolite modules with the brain volume components. We found five metabolite modules and 125 single metabolites with significant differences between sexes. These results highlight a differential lipid disruption in AD progression between sexes. Men showed a greater negative association of phosphatidylcholines and sphingomyelins and a positive association of VLDL and large LDL with AD progression. In contrast, women showed a positive association of triglycerides in VLDL and small and medium LDL with AD progression. Explicitly identifying sex differences in metabolomics during AD progression can highlight particular metabolic disruptions in each sex. Our research study and strategy can lead to better-tailored studies and better-suited treatments that take sex differences into account.

Dataset Information

Divergence in the metabolome between natural aging and Alzheimer's disease.

Publications

Divergence in the metabolome between natural aging and Alzheimer's disease.

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