Alterations in cholesterol metabolism as a risk factor for developing Alzheimer’s disease: Potential novel targets for treatment

Loera-Valencia, Raúl; Goikolea, Julen; Parrado-Fernández, Cristina; Merino‐Serrais, Paula; Maioli, Silvia

doi:10.1016/j.jsbmb.2019.03.003

Cited by 182 publications

(147 citation statements)

References 165 publications

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“…Brain tissues contain large amounts of cholesterol, up to 25% of the body's cholesterol content (Dietschy, 2009) whose metabolism and complex homeostasis regulation in the CNS remain unclear (Zhang and Liu, 2015). Changes in this metabolism are related to neurodegenerative pathologies, such as Alzheimer's disease (AD), Parkinson's disease, and Huntington's disease, as well as to age-related cognitive decline (Petrov et al, 2016;Loera-Valencia et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Soluble Epoxide Hydrolase and Brain Cholesterol Metabolism

Domingues

Callai-Silva

Piovesan

et al. 2020

Front. Mol. Neurosci.

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Section: Introductionmentioning

confidence: 99%

Soluble Epoxide Hydrolase and Brain Cholesterol Metabolism

Domingues

Callai-Silva

Piovesan

et al. 2020

Front. Mol. Neurosci.

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“…The ten-best combinations of the eleven drugs that excluded drugs acting on the brain and/or psychological level included NSAID, anticoagulant, lipid-lowering, and antihypertensive drugs, as well as estrogen/progestin. Drugs from all of these categories have been shown to reduce AD risk, however they all show disappointing results when used singly in clinical trials [33][34][35][36][37][38][39][40][41][42][43][44][45]. The use of combinations of these and other drugs remains an attractive option [7][8][9][10][11].…”

Section: Discussionmentioning

confidence: 99%

Predicting the potency of anti-Alzheimer drug combinations using machine learning

Anastasio

2020

Preprint

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BACKGROUND: Clinical trials of single drugs for the treatment of Alzheimer Disease (AD) have been notoriously unsuccessful. Combinations of repurposed drugs could provide effective treatments for AD.The challenge is to identify potentially potent combinations.OBJECTIVE: To use machine learning (ML) to extract the knowledge from two leading AD databases, and then use the machine to predict which combinations of the drugs in common between the two databases would be the most effective as treatments for AD.METHODS: Three-layered neural networks (NNs) having compound, gated units in their internal layer were trained using ML to predict the cognitive scores of participants in either database, given the other data fields including age, demographic variables, comorbidities, and drugs taken. RESULTS:The predictions from the separately trained NNs were strongly correlated. The best drug combinations, jointed determined from both sets of predictions, were high in NSAID, anticoagulant, lipid-lowering, and antihypertensive drugs, and female hormones. CONCLUSION:The results suggest that AD, as a multifactorial disorder, could be effectively treated using a combination of repurposed drugs.

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“…High cholesterol is a known risk factor for Alzheimer's disease, but cholesterols themselves cannot penetrate the blood brain barrier, making the mechanism by which hypercholesterolemia contributes to the disease somewhat obscure. On the other hand, oxysterols, which are oxidized cholesterol metabolites, are able to enter the brain [20].…”

Section: Highly Disrupted Genesmentioning

confidence: 99%

Network Medicine Approach for Analysis of Alzheimer’s Disease Gene Expression Data

Cohen

Pilozzi

Huang

2020

IJMS

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Alzheimer’s disease (AD) is the most widespread diagnosed cause of dementia in the elderly. It is a progressive neurodegenerative disease that causes memory loss as well as other detrimental symptoms that are ultimately fatal. Due to the urgent nature of this disease, and the current lack of success in treatment and prevention, it is vital that different methods and approaches are applied to its study in order to better understand its underlying mechanisms. To this end, we have conducted network-based gene co-expression analysis on data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. By processing and filtering gene expression data taken from the blood samples of subjects with varying disease states and constructing networks based on that data to evaluate gene relationships, we have been able to learn about gene expression correlated with the disease, and we have identified several areas of potential research interest.

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Alterations in cholesterol metabolism as a risk factor for developing Alzheimer’s disease: Potential novel targets for treatment

Cited by 182 publications

References 165 publications

Soluble Epoxide Hydrolase and Brain Cholesterol Metabolism

Soluble Epoxide Hydrolase and Brain Cholesterol Metabolism

Predicting the potency of anti-Alzheimer drug combinations using machine learning

Network Medicine Approach for Analysis of Alzheimer’s Disease Gene Expression Data

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