Diclofenac reduces the risk of Alzheimer’s disease: a pilot analysis of NSAIDs in two US veteran populations

Stüve, Olaf; Weideman, Rick; McMahan, Danni; Jacob, David; Little, Bertis B.

doi:10.1177/1756286420935676

Cited by 34 publications

(33 citation statements)

References 52 publications

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“…Diclofenac is chemically related to the finasteride class of NSAIDs and has been shown to improve cognition in two independent studies using mouse models of AD ( Joo et al, 2006 ; Daniels et al, 2016 ). It is also associated with a reduced risk of developing AD ( Stuve et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Landscape of immune infiltration in entorhinal cortex of patients with Alzheimerʼs disease

Zhang

Cao

et al. 2022

Front. Pharmacol.

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Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases and manifests as progressive memory loss and cognitive dysfunction. Neuroinflammation plays an important role in the development of Alzheimer’s disease and anti-inflammatory drugs reduce the risk of the disease. However, the immune microenvironment in the brains of patients with Alzheimer’s disease remains unclear, and the mechanisms by which anti-inflammatory drugs improve Alzheimer’s disease have not been clearly elucidated. This study aimed to provide an overview of the immune cell composition in the entorhinal cortex of patients with Alzheimer’s disease based on the transcriptomes and signature genes of different immune cells and to explore potential therapeutic targets based on the relevance of drug targets. Transcriptomics data from the entorhinal cortex tissue, derived from GSE118553, were used to support our study. We compared the immune-related differentially expressed genes (irDEGs) between patients and controls by using the limma R package. The difference in immune cell composition between patients and controls was detected via the xCell algorithm based on the marker genes in immune cells. The correlation between marker genes and immune cells and the interaction between genes and drug targets were evaluated to explore potential therapeutic target genes and drugs. There were 81 irDEGs between patients and controls that participated in several immune-related pathways. xCell analysis showed that most lymphocyte scores decreased in Alzheimer’s disease, including CD4+ Tc, CD4+ Te, Th1, natural killer (NK), natural killer T (NKT), pro-B cells, eosinophils, and regulatory T cells, except for Th2 cells. In contrast, most myeloid cell scores increased in patients, except in dendritic cells. They included basophils, mast cells, plasma cells, and macrophages. Correlation analysis suggested that 37 genes were associated with these cells involved in innate immunity, of which eight genes were drug targets. Taken together, these results delineate the profile of the immune components of the entorhinal cortex in Alzheimer’s diseases, providing a new perspective on the development and treatment of Alzheimer’s disease.

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Section: Discussionmentioning

confidence: 99%

Landscape of immune infiltration in entorhinal cortex of patients with Alzheimerʼs disease

Zhang

Cao

et al. 2022

Front. Pharmacol.

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“…In a model of Aβ-induced memory loss and a transgenic mouse model of AD, fenamate NSAIDs inhibited cognitive impairment and demonstrated both protective and therapeutic efficacies. Diclofenac, a structurally related NSAID, was similarly shown to reduce levels of IL-1β [ 88 ]. Based on the clinical availability of fenamate NSAIDs, repurposing them as NLRP3 inhibitors may provide a rapid therapeutic AD treatment strategy.…”

Section: Nlrp3mentioning

confidence: 99%

New Insights into Neuroinflammation Involved in Pathogenic Mechanism of Alzheimer’s Disease and Its Potential for Therapeutic Intervention

Pember

et al. 2022

Cells

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Alzheimer’s disease (AD) is the most common form of dementia, affecting more than 50 million people worldwide with an estimated increase to 139 million people by 2050. The exact pathogenic mechanisms of AD remain elusive, resulting in the fact that the current therapeutics solely focus on symptomatic management instead of preventative or curative strategies. The two most widely accepted pathogenic mechanisms of AD include the amyloid and tau hypotheses. However, it is evident that these hypotheses cannot fully explain neuronal degeneration shown in AD. Substantial evidence is growing for the vital role of neuroinflammation in AD pathology. The neuroinflammatory hypothesis provides a new, exciting lead in uncovering the underlying mechanisms contributing to AD. This review aims to highlight new insights into the role of neuroinflammation in the pathogenesis of AD, mainly including the involvement of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), nucleotide-binding oligomerization domain, leucine-rich repeat-containing protein 3 (NLRP3)/caspase-1 axis, triggering receptor expressed on myeloid cells 2 (TREM2) and cGAS-STING as key influencers in augmenting AD development. The inflammasomes related to the pathways of NF-κB, NLRP3, TREM2, and cGAS-STING as biomarkers of the neuroinflammation associated with AD, as well as an overview of novel AD treatments based on these biomarkers as potential drug targets reported in the literature or under clinical trials, are explored.

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“…(39) In pilot studies, diclofenac showed positive effects to reduce the risk of AD in veterans. (40) Rivastigmine is an AChE and butyrylcholinesterase inhibitor, used to treat memory loss and to improve thinking abilities in AD patients. The tolerability and safety of rivastigmine or in combination with lithium, vitamin E, and diclofenac was tested, observing that these drugs were well-tolerated by cholinergic neurons.…”

Section: In Vitro Validation On Safety Tolerability and Neuronal Resp...mentioning

confidence: 99%

Multi-Task Graph Neural Network For Alzheimer’s Disease Drug Repurposing Using Knowledge Graph And Multi-Level Evidence

Hsieh

Plascencia-Villa

Lin³

et al. 2022

Preprint

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Developing drugs for treating Alzheimer’s disease (AD) has been extremely challenging and costly due to limited knowledge on underlying biological mechanisms and therapeutic targets. Repurposing drugs or their combination has shown potential in accelerating drug development due to the reduced drug toxicity while targeting multiple pathologies. To address the challenge in AD drug development, we developed a multi-task machine learning pipeline to integrate a comprehensive knowledge graph on biological/pharmacological interactions and multi-level evidence on drug efficacy, to identify repurposable drugs and their combination candidates. We developed and computationally validated a heterogeneous graph representation model with transfer learning from universal biomedical databases and with joint optimization with AD risk genes. Using the drug embedding from the heterogeneous graph representation model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, mechanistic efficacy in preclinical models, population-based treatment effect, and Phase II/III clinical trials. We experimentally validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in reducing oxidative stress and safety in maintaining neuronal viability and morphology. Our neuronal response experiments confirmed several biologically efficacious drug combinations. This pipeline showed that harmonizing heterogeneous and complementary data/knowledge, including human interactome, transcriptome patterns, experimental efficacy, and real-world patient data shed light on the drug development of complex diseases.

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Diclofenac reduces the risk of Alzheimer’s disease: a pilot analysis of NSAIDs in two US veteran populations

Cited by 34 publications

References 52 publications

Landscape of immune infiltration in entorhinal cortex of patients with Alzheimerʼs disease

Landscape of immune infiltration in entorhinal cortex of patients with Alzheimerʼs disease

New Insights into Neuroinflammation Involved in Pathogenic Mechanism of Alzheimer’s Disease and Its Potential for Therapeutic Intervention

Multi-Task Graph Neural Network For Alzheimer’s Disease Drug Repurposing Using Knowledge Graph And Multi-Level Evidence

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