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As populations age, prevalence of Alzheimer's disease (AD) is rising. Over 100 years of research has provided valuable insights into the pathophysiology of the disease, for which age is the principal risk factor. However, in recent years, a multitude of clinical trial failures has led to pharmaceutical corporations becoming more and more unwilling to support drug development in AD. It is possible that dependence on the amyloid cascade hypothesis as a guide for preclinical research and drug discovery is part of the problem. Accumulating evidence suggests that amyloid plaques and tau tangles are evident in non-demented individuals and that reducing or clearing these lesions does not always result in clinical improvement. Normal aging is associated with pathologies and cognitive decline that are similar to those observed in AD, making differentiation of AD-related cognitive decline and neuropathology challenging. In this mini-review, we discuss the difficulties with discerning normal, age-related cognitive decline with that related to AD. We also discuss some neuropathological features of AD and aging, including amyloid and tau pathology, synapse loss, inflammation and insulin signaling in the brain, with a view to highlighting cognitive or neuropathological markers that distinguish AD from normal aging. It is hoped that this review will help to bolster future preclinical research and support the development of clinical tools and therapeutics for AD.

Introduction We assessed the association of self‐reported hearing impairment and hearing aid use with cognitive decline and progression to mild cognitive impairment (MCI). Methods We used a large referral‐based cohort of 4358 participants obtained from the National Alzheimer's Coordinating Center. The standard covariate‐adjusted Cox proportional hazards model, the marginal structural Cox model with inverse probability weighting, standardized Kaplan‐Meier curves, and linear mixed‐effects models were applied to test the hypotheses. Results Hearing impairment was associated with increased risk of MCI (standardized hazard ratio [HR] 2.58, 95% confidence interval [CI: 1.73 to 3.84], P = .004) and an accelerated rate of cognitive decline (P < .001). Hearing aid users were less likely to develop MCI than hearing‐impaired individuals who did not use a hearing aid (HR 0.47, 95% CI [0.29 to 0.74], P = .001). No difference in risk of MCI was observed between individuals with normal hearing and hearing‐impaired adults using hearing aids (HR 0.86, 95% CI [0.56 to 1.34], P = .51). Discussion Use of hearing aids may help mitigate cognitive decline associated with hearing loss.

Previously, we have developed a retro-inverso peptide inhibitor (RI-OR2, rGffvlkGr) that blocks the in vitro formation and toxicity of the Aβ oligomers which are thought to be a cause of neurodegeneration and memory loss in Alzheimer's disease. We have now attached a retro-inverted version of the HIV protein transduction domain 'TAT' to RI-OR2 to target this new inhibitor (RI-OR2-TAT, Ac-rGffvlkGrrrrqrrkkrGy-NH(2)) into the brain. Following its peripheral injection, a fluorescein-labelled version of RI-OR2-TAT was found to cross the blood brain barrier and bind to the amyloid plaques and activated microglial cells present in the cerebral cortex of 17-months-old APPswe/PS1ΔE9 transgenic mice. Daily intraperitoneal injection of RI-OR2-TAT (at 100 nmol/kg) for 21 days into 10-months-old APPswe/PS1ΔE9 mice resulted in a 25% reduction (p<0.01) in the cerebral cortex of Aβ oligomer levels, a 32% reduction (p<0.0001) of β-amyloid plaque count, a 44% reduction (p<0.0001) in the numbers of activated microglial cells, and a 25% reduction (p<0.0001) in oxidative damage, while the number of young neurons in the dentate gyrus was increased by 210% (p<0.0001), all compared to control APPswe/PS1ΔE9 mice injected with vehicle (saline) alone. Our data suggest that oxidative damage, inflammation, and inhibition of neurogenesis are all a downstream consequence of Aβ aggregation, and identify a novel brain-penetrant retro-inverso peptide inhibitor of Aβ oligomer formation for further testing in humans as a potential disease-modifying treatment for Alzheimer's disease.

Certain endogenous bile acids have been proposed as potential therapies for ameliorating Alzheimer’s disease (AD) but their role, if any, in the pathophysiology of this disease is not currently known. Given recent evidence of bile acids having protective and anti-inflammatory effects on the brain, it is important to establish how AD affects levels of endogenous bile acids. Using LC-MS/MS, this study profiled 22 bile acids in brain extracts and blood plasma from AD patients (n = 10) and age-matched control subjects (n = 10). In addition, we also profiled brain/plasma samples from APP/PS1 and WT mice (aged 6 and 12 months). In human plasma, we detected significantly lower cholic acid (CA, p = 0.03) in AD patients than age-matched control subjects. In APP/PS1 mouse plasma we detected higher CA (p = 0.05, 6 months) and lower hyodeoxycholic acid (p = 0.04, 12 months) than WT. In human brain with AD pathology (Braak stages V-VI) taurocholic acid (TCA) were significantly lower (p = 0.01) than age-matched control subjects. In APP/PS1 mice we detected higher brain lithocholic acid (p = 0.05) and lower tauromuricholic acid (TMCA; p = 0.05, 6 months). TMCA was also decreased (p = 0.002) in 12-month-old APP/PS1 mice along with 5 other acids: CA (p = 0.02), β-muricholic acid (p = 0.02), Ω-muricholic acid (p = 0.05), TCA (p = 0.04), and tauroursodeoxycholic acid (p = 0.02). The levels of bile acids are clearly disturbed during the development of AD pathology and, since some bile acids are being proposed as potential AD therapeutics, we demonstrate a method that can be used to support work to advance bile acid therapeutics.

Background Dementia is caused by a variety of neurodegenerative diseases and is associated with a decline in memory and other cognitive abilities, while inflicting an enormous socioeconomic burden. The complexity of dementia and its associated comorbidities presents immense challenges for dementia research and care, particularly in clinical decision-making. Main body Despite the lack of disease-modifying therapies, there is an increasing and urgent need to make timely and accurate clinical decisions in dementia diagnosis and prognosis to allow appropriate care and treatment. However, the dementia care pathway is currently suboptimal. We propose that through computational approaches, understanding of dementia aetiology could be improved, and dementia assessments could be more standardised, objective and efficient. In particular, we suggest that these will involve appropriate data infrastructure, the use of data-driven computational neurology approaches and the development of practical clinical decision support systems. We also discuss the technical, structural, economic, political and policy-making challenges that accompany such implementations. Conclusion The data-driven era for dementia research has arrived with the potential to transform the healthcare system, creating a more efficient, transparent and personalised service for dementia.

Background  Dementia is a group of symptoms that largely affects older people. The majority of patients face behavioural and psychological symptoms (BPSD) during the course of their illness. Alzheimer’s disease (AD) and vascular dementia (VaD) are two of the most prevalent types of dementia. Available medications provide symptomatic benefits and provide relief from BPSD and associated health issues. However, it is unclear how specific dementia, antidepressant, antipsychotic, antianxiety, and mood stabiliser drugs, used in the treatment of depression and dementia subtypes are prescribed in hospital admission, during hospital stay, and at the time of discharge. To address this, we apply multi-dimensional data analytical approaches to understand drug prescribing practices within hospitals in England and Wales. Methods We made use of the UK National Audit of Dementia (NAD) dataset and pre-processed the dataset. We evaluated the pairwise Pearson correlation of the dataset and selected key data features which are highly correlated with dementia subtypes. After that, we selected drug prescribing behaviours (e.g. specific medications at the time of admission, during the hospital stay, and upon discharge), drugs and disorders. Then to shed light on the relations across multiple features or dimensions, we carried out multiple regression analyses, considering the number of dementia, antidepressant, antipsychotic, antianxiety, mood stabiliser, and antiepileptic/anticonvulsant drug prescriptions as dependent variables, and the prescription of other drugs, number of patients with dementia subtypes (AD/VaD), and depression as independent variables. Results In terms of antidepressant drugs prescribed in hospital admission, during stay and discharge, the number of sertraline and venlafaxine prescriptions were associated with the number of VaD patients whilst the number of mirtazapine prescriptions was associated with frontotemporal dementia patients. During admission, the number of lamotrigine prescriptions was associated with frontotemporal dementia patients, and with the number of valproate and dosulepin prescriptions. During discharge, the number of mirtazapine prescriptions was associated with the number of donepezil prescriptions in conjunction with frontotemporal dementia patients. Finally, the number of prescriptions of donepezil/memantine at admission, during hospital stay and at discharge exhibited positive association with AD patients. Conclusion Our analyses reveal a complex, multifaceted set of interactions among prescribed drug types, dementia subtypes, and depression.

Alzheimer’s disease (AD) is a complex neurodegenerative condition that is characterized by the build-up of amyloid-beta plaques and neurofibrillary tangles. While multiple theories explaining the aetiology of the disease have been suggested, the underlying cause of the disease is still unknown. Despite this, several modifiable and non-modifiable factors that increase the risk of developing AD have been identified. To date, only eight AD drugs have ever gained regulatory approval, including six symptomatic and two disease-modifying drugs. However, not all are available in all countries and high costs associated with new disease-modifying biologics prevent large proportions of the patient population from accessing them. With the current patient population expected to triple by 2050, it is imperative that new, effective, and affordable drugs become available to patients. Traditional drug development strategies have a 99% failure rate in AD, which is far higher than in other disease areas. Even when a drug does reach the market, additional barriers such as high cost and lack of accessibility prevent patients from benefiting from them. In this review, we discuss how a stratified medicine drug repurposing approach may address some of the limitations and barriers that traditional strategies face in relation to drug development in AD. We believe that novel, stratified drug repurposing studies may expedite the discovery of alternative, effective, and more affordable treatment options for a rapidly expanding patient population in comparison with traditional drug development methods.

Introduction Hearing aid usage has been linked to improvements in cognition, communication, and socialization, but the extent to which it can affect the incidence and progression of dementia is unknown. Such research is vital given the high prevalence of dementia and hearing impairment in older adults, and the fact that both conditions often coexist. In this study, we examined for the first time the effect of the use of hearing aids on the conversion from mild cognitive impairment (MCI) to dementia and progression of dementia. Methods We used a large referral‐based cohort of 2114 hearing‐impaired patients obtained from the National Alzheimer's Coordinating Center. Survival analyses using multivariable Cox proportional hazards regression model and weighted Cox regression model with censored data were performed to assess the effect of hearing aid use on the risk of conversion from MCI to dementia and risk of death in hearing‐impaired participants. Disease progression was assessed with Clinical Dementia Rating Sum of Boxes (CDR‐SB) scores. Three types of sensitivity analyses were performed to validate the robustness of the results. Results MCI participants that used hearing aids were at significantly lower risk of developing all‐cause dementia compared to those not using hearing aids (hazard ratio [HR] 0.73, 95% confidence interval [CI], 0.61 to 0.89; false discovery rate [FDR] P = 0.004). The mean annual rate of change (standard deviation) in CDR‐SB scores for hearing aid users with MCI was 1.3 (1.45) points and significantly lower than for individuals not wearing hearing aids with a 1.7 (1.95) point increase in CDR‐SB per year (P = 0.02). No association between hearing aid use and risk of death was observed. Our findings were robust subject to sensitivity analyses. Discussion Among hearing‐impaired adults, hearing aid use was independently associated with reduced dementia risk. The causality between hearing aid use and incident dementia should be further tested.

Missing Alzheimer's disease (AD) data is prevalent and poses significant challenges for AD diagnosis. Previous studies have explored various data imputation approaches on AD data, but the systematic evaluation of deep learning algorithms for imputing heterogeneous and comprehensive AD data is limited. This study investigates the efficacy of denoising autoencoder‐based imputation of missing key features of heterogeneous data that comprised tau‐PET, MRI, cognitive and functional assessments, genotype, sociodemographic, and medical history. The authors focused on extreme (≥40%) missing at random of key features which depend on AD progression; identified as the history of a mother having AD, APoE ε4 alleles, and clinical dementia rating. Along with features selected using traditional feature selection methods, latent features extracted from the denoising autoencoder are incorporated for subsequent classification. Using random forest classification with 10‐fold cross‐validation, robust AD predictive performance of imputed datasets (accuracy: 79%–85%; precision: 71%–85%) across missingness levels, and high recall values with 40% missingness are found. Further, the feature‐selected dataset using feature selection methods, including autoencoder, demonstrated higher classification score than that of the original complete dataset. These results highlight the effectiveness and robustness of autoencoder in imputing crucial information for reliable AD prediction in AI‐based clinical decision support systems. Denoising autoencoder imputation of key heterogenous Alzheimer's disease (AD) data features, with extreme proportion of missingness, leads to high AD predictive performance. Machine learning prediction using machine‐selected features, including autoencoder's latent features, outperforms the prediction using the original complete dataset.

Biomarkers for Alzheimer's disease (AD) diagnosis do not always correlate reliably with cognitive symptoms, making clinical diagnosis inconsistent. In this study, the performance of a graphical neural network (GNN) classifier based on data‐driven diagnostic classes from unsupervised clustering on heterogeneous data is compared to the performance of a classifier using clinician diagnosis as an outcome. Unsupervised clustering on tau‐positron emission tomography (PET) and cognitive and functional assessment data was performed. Five clusters embedded in a non‐linear uniform manifold approximation and project (UMAP) space were identified. The individual clusters revealed specific feature characteristics with respect to clinical diagnosis of AD, gender, family history, age, and underlying neurological risk factors (NRFs). In particular, one cluster comprised mainly diagnosed AD cases. All cases within this cluster were re‐labelled AD cases. The re‐labelled cases are characterized by high cerebrospinal fluid amyloid beta (CSF Aβ) levels at a younger age, even though Aβ data was not used for clustering. A GNN model was trained using the re‐labelled data with a multiclass area‐under‐the‐curve (AUC) of 95.2%, higher than the AUC of a GNN trained on clinician diagnosis (91.7%; p = 0.02). Overall, our work suggests that more objective cluster‐based diagnostic labels combined with GNN classification may have value in clinical risk stratification and diagnosis of AD.