Abstract
Background: Neurodegenerative Diseases (NDs) are age-dependent and include Alzheimer’s disease (AD), Parkinson’s disease (PD), progressive supranuclear palsy (PSP), frontotemporal dementia (FTD), and so on. There have been numerous studies showing that accelerated aging is closely related (even the driver of) ND, thus promoting imbalances in cellular homeostasis. However, the mechanisms of how different ND types are related/triggered by advanced aging are still unclear. Therefore, there is an urgent need to explore the potential markers/mechanisms of different ND types based on aging acceleration at a system level.Methods: AD, PD, PSP, FTD, and aging markers were identified by supervised machine learning methods. The aging acceleration differential networks were constructed based on the aging score. Both the enrichment analysis and sensitivity analysis were carried out to investigate both common and specific mechanisms among different ND types in the context of aging acceleration.Results: The extracellular fluid, cellular metabolisms, and inflammatory response were identified as the common driving factors of cellular homeostasis imbalances during the accelerated aging process. In addition, Ca ion imbalance, abnormal protein depositions, DNA damage, and cytoplasmic DNA in macrophages were also revealed to be special mechanisms that further promote AD, PD, PSP, and FTD, respectively.Conclusion: The accelerated epigenetic aging mechanisms of different ND types were integrated and compared through our computational pipeline.
Highlights
With the further extension of human life, the number of elderly people is increasing and the incidence rate of senile neurodegenerative diseases (ND) is rising (Kovacs, 2017)
The aging predictor and disease predictor were modeled by the classification ensemble algorithm of the classification tree, and the best predictor was selected based on 10-fold cross-validation (Table 1 and Supplementary Text 1)
According to the cross-validation results, the top 46, 35, 14, 23, and 34 dimensions of cpg sites were identified as risk biomarkers related to aging, Alzheimer’s disease (AD), Parkinson’s disease (PD), progressive Supranuclear Palsy (PSP), and frontotemporal dementia (FTD), respectively
Summary
With the further extension of human life, the number of elderly people is increasing and the incidence rate of senile neurodegenerative diseases (ND) is rising (Kovacs, 2017). Human life expectancy has been improved in recent years, ND have become the most common diseases affecting elderly populations (Heemels, 2016), with a large number of people being. Comparing ND Mechanisms by Aging affected by Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal dementia (FTD), and progressive Supranuclear Palsy (PSP). A large amount of epidemiological evidence has shown that NDs are closely related to advanced brain aging, which is often considered to be one of the driving factors of ND (Mayne et al, 2020). Neurodegenerative Diseases (NDs) are age-dependent and include Alzheimer’s disease (AD), Parkinson’s disease (PD), progressive supranuclear palsy (PSP), frontotemporal dementia (FTD), and so on. There is an urgent need to explore the potential markers/mechanisms of different ND types based on aging acceleration at a system level
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