Alzheimer disease

Knopman, David S.; Amieva, Hélène; Petersen, Ronald C.; Chételat, Gaël; Holtzman, David M.; Hyman, Bradley T.; Nixon, Ralph A.; Jones, David T.

doi:10.1038/s41572-021-00269-y

Cited by 1,123 publications

(1,082 citation statements)

References 289 publications

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“…Currently, there are 44 million people dealing with dementia, being the second leading cause of death in people aged 70 and over [1][2][3][4]. Alzheimer's disease (AD) is the most prevalent form of dementia [1] and it is also one of the most significant causes of morbidity and mortality in the elderly population worldwide [3].…”

Section: Introductionmentioning

confidence: 99%

“…Alzheimer's disease (AD) is the most prevalent form of dementia [1] and it is also one of the most significant causes of morbidity and mortality in the elderly population worldwide [3]. AD prevalence is estimated to reach Pharmaceuticals 2021, 14, 890 2 of 22 115 million by 2050 due to an increased ageing population pattern, unless novel drugs are available to slow or cure this disease [2][3][4]. Neuropathological modifications of AD, such as tau hyperphosphorylation and Aβ toxicity, led to the main current hypothesis trying to explain neuronal and synapse loss, associated with cognitive and memory impairment [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Metformin a Potential Pharmacological Strategy in Late Onset Alzheimer’s Disease Treatment

et al. 2021

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Alzheimer’s disease (AD) is one of the most devastating brain disorders. Currently, there are no effective treatments to stop the disease progression and it is becoming a major public health concern. Several risk factors are involved in the progression of AD, modifying neuronal circuits and brain cognition, and eventually leading to neuronal death. Among them, obesity and type 2 diabetes mellitus (T2DM) have attracted increasing attention, since brain insulin resistance can contribute to neurodegeneration. Consequently, AD has been referred to “type 3 diabetes” and antidiabetic medications such as intranasal insulin, glitazones, metformin or liraglutide are being tested as possible alternatives. Metformin, a first line antihyperglycemic medication, is a 5′-adenosine monophosphate (AMP)-activated protein kinase (AMPK) activator hypothesized to act as a geroprotective agent. However, studies on its association with age-related cognitive decline have shown controversial results with positive and negative findings. In spite of this, metformin shows positive benefits such as anti-inflammatory effects, accelerated neurogenesis, strengthened memory, and prolonged life expectancy. Moreover, it has been recently demonstrated that metformin enhances synaptophysin, sirtuin-1, AMPK, and brain-derived neuronal factor (BDNF) immunoreactivity, which are essential markers of plasticity. The present review discusses the numerous studies which have explored (1) the neuropathological hallmarks of AD, (2) association of type 2 diabetes with AD, and (3) the potential therapeutic effects of metformin on AD and preclinical models.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Metformin a Potential Pharmacological Strategy in Late Onset Alzheimer’s Disease Treatment

et al. 2021

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“…The clinical diagnosis of AD is based on the presence of objective cognitive deficits (which are, typically, prominent memory impairments). In some cases, AD may show atypical presentations, with impairments in non-amnesic domains (i.e., attention, executive functions, visuo-constructive practice and language) [1]. However, AD shares many common clinical features with other neurodegenerative dementia, including Lewy body dementia [2], frontotemporal disorders [3], and vascular dementia, making early and differential diagnosis difficult, especially in the first stage of the disease [4,5].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge?

et al. 2021

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Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer’s disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.

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“…Although the pathogenesis of AD remains unclear, a large number of clinical and preclinical experiments have shown that the occurrence and development of AD were closely related to Aβ toxic injury, abnormal phosphorylation of tau protein, inflammatory reaction, free radical damage, and so on (Chiti and Dobson, 2017;Li et al, 2018). According to the preliminary studies, the pathogenesis of AD was mainly related to Aβ plaques, but the therapeutic strategies aimed at reducing β-amyloid protein failed one after another (Loureiro et al, 2020;Knopman et al, 2021). Similarly, there was no breakthrough in the study on the phosphorylation of tau protein (Hane et al, 2017;Morris et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Identification of Multi-Target Anti-AD Chemical Constituents From Traditional Chinese Medicine Formulae by Integrating Virtual Screening and In Vitro Validation

et al. 2021

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Alzheimer’s disease (AD) is a neurodegenerative disease that seriously threatens the health of the elderly. At present, no drugs have been proven to cure or delay the progression of the disease. Due to the multifactorial aetiology of this disease, the multi-target-directed ligand (MTDL) approach provides an innovative and promising idea in search for new drugs against AD. In order to find potential multi-target anti-AD drugs from traditional Chinese medicine (TCM) formulae, a compound database derived from anti-AD Chinese herbal formulae was constructed and predicted by the anti-AD multi-target drug prediction platform established in our laboratory. By analyzing the results of virtual screening, 226 chemical constituents with 3 or more potential AD-related targets were collected, from which 16 compounds that were predicted to combat AD through various mechanisms were chosen for biological validation. Several cell models were established to validate the anti-AD effects of these compounds, including KCl, Aβ, okadaic acid (OA), SNP and H2O2 induced SH-SY5Y cell model and LPS induced BV2 microglia model. The experimental results showed that 12 compounds including Nonivamide, Bavachromene and 3,4-Dimethoxycinnamic acid could protect model cells from AD-related damages and showed potential anti-AD activity. Furthermore, the potential targets of Nonivamide were investigated by molecular docking study and analysis with CDOCKER revealed the possible binding mode of Nonivamide with its predicted targets. In summary, 12 potential multi-target anti-AD compounds have been found from anti-AD TCM formulae by comprehensive application of computational prediction, molecular docking method and biological validation, which laid a theoretical and experimental foundation for in-depth study, also providing important information and new research ideas for the discovery of anti-AD compounds from traditional Chinese medicine.

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Alzheimer disease

Cited by 1,123 publications

References 289 publications

Metformin a Potential Pharmacological Strategy in Late Onset Alzheimer’s Disease Treatment

Metformin a Potential Pharmacological Strategy in Late Onset Alzheimer’s Disease Treatment

Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge?

Identification of Multi-Target Anti-AD Chemical Constituents From Traditional Chinese Medicine Formulae by Integrating Virtual Screening and In Vitro Validation

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