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Astrocytes are abundantly and ubiquitously expressed cell types with diverse functions throughout the central nervous system. Astrocytes show remarkable plasticity and exhibit morphological, molecular, and functional remodeling in response to injury, disease, or infection of the central nervous system, as evident in neurodegenerative diseases. Astroglial mediated inflammation plays a prominent role in the pathogenesis of neurodegenerative diseases. This review focus on the role of astrocytes as essential players in neuroinflammation and discuss their morphological and functional heterogeneity in the normal central nervous system and explore the spatial and temporal variations in astroglial phenotypes observed under different disease conditions. This review discusses the intimate relationship of astrocytes to pathological hallmarks of neurodegenerative diseases. Finally, this review considers the putative therapeutic strategies that can be deployed to modulate the astroglial functions in neurodegenerative diseases. Highlights Astroglia mediated neuroinflammation plays a key role in the pathogenesis of neurodegenerative diseases. Activated astrocytes exhibit diverse phenotypes in a region‐specific manner in brain and interact with β‐amyloid, tau, and α‐synuclein species as well as with microglia and neuronal circuits. Activated astrocytes are likely to influence the trajectory of disease progression of neurodegenerative diseases, as determined by the stage of disease, individual susceptibility, and state of astroglial priming. Modulation of astroglial activation may be a therapeutic strategy at various stages in the trajectory of neurodegenerative diseases to modify the disease course.

Alzheimer disease (AD) is the most common form of neurodegenerative disease, estimated to contribute 60–70% of all cases of dementia worldwide. According to the prevailing amyloid cascade hypothesis, amyloid-β (Aβ) deposition in the brain is the initiating event in AD, although evidence is accumulating that this hypothesis is insufficient to explain many aspects of AD pathogenesis. The discovery of increased levels of inflammatory markers in patients with AD and the identification of AD risk genes associated with innate immune functions suggest that neuroinflammation has a prominent role in the pathogenesis of AD. In this Review, we discuss the interrelationships between neuroinflammation and amyloid and tau pathologies as well as the effect of neuroinflammation on the disease trajectory in AD. We specifically focus on microglia as major players in neuroinflammation and discuss the spatial and temporal variations in microglial phenotypes that are observed under different conditions. We also consider how these cells could be modulated as a therapeutic strategy for AD.Accumulating evidence indicates important roles for microglial activation and neuroinflammation in Alzheimer disease (AD). Here, Leng and Edison describe the interplay between microglial activation and AD-related pathologies and discuss how microglial priming and activation might influence the trajectory of AD.

A growing body of evidence suggests that cardiometabolic risk factors play a significant role in Alzheimer’s disease (AD). Diabetes, obesity and hypertension are highly prevalent and can accelerate neurodegeneration and perpetuate the burden of AD. Insulin resistance and enzymes including insulin degrading enzymes are implicated in AD where breakdown of insulin is prioritised over amyloid-β. Leptin resistance and inflammation demonstrated by higher plasma and central nervous system levels of interleukin-6 (IL-6), IL-1β and tumour necrosis factor-α, are mechanisms connecting obesity and diabetes with AD. Leptin has been shown to ameliorate AD pathology and enhance long-term potentiation and hippocampal-dependent cognitive function. The renin-aldosterone angiotensin system, involved in hypertension, has been associated with AD pathology and neurotoxic reactive oxygen species, where angiotensin binds to specific angiotensin-1 receptors in the hippocampus and cerebral cortex. This review aims to consolidate the evidence behind putative processes stimulated by obesity, diabetes and hypertension, which leads to increased AD risk. We focus on how novel knowledge can be applied clinically to facilitate recognition of efficacious treatment strategies for AD.

AbstractSince its emergence in Wuhan, China, covid-19 has spread and had a profound effect on the lives and health of people around the globe. As of 4 July 2021, more than 183 million confirmed cases of covid-19 had been recorded worldwide, and 3.97 million deaths. Recent evidence has shown that a range of persistent symptoms can remain long after the acute SARS-CoV-2 infection, and this condition is now coined long covid by recognized research institutes. Studies have shown that long covid can affect the whole spectrum of people with covid-19, from those with very mild acute disease to the most severe forms. Like acute covid-19, long covid can involve multiple organs and can affect many systems including, but not limited to, the respiratory, cardiovascular, neurological, gastrointestinal, and musculoskeletal systems. The symptoms of long covid include fatigue, dyspnea, cardiac abnormalities, cognitive impairment, sleep disturbances, symptoms of post-traumatic stress disorder, muscle pain, concentration problems, and headache. This review summarizes studies of the long term effects of covid-19 in hospitalized and non-hospitalized patients and describes the persistent symptoms they endure. Risk factors for acute covid-19 and long covid and possible therapeutic options are also discussed.

Compelling experimental evidence suggests that microglial activation is involved in the spread of tau tangles over the neocortex in Alzheimer’s disease (AD). We tested the hypothesis that the spatial propagation of microglial activation and tau accumulation colocalize in a Braak-like pattern in the living human brain. We studied 130 individuals across the aging and AD clinical spectrum with positron emission tomography brain imaging for microglial activation ([ 11 C]PBR28), amyloid-β (Aβ) ([ 18 F]AZD4694) and tau ([ 18 F]MK-6240) pathologies. We further assessed microglial triggering receptor expressed on myeloid cells 2 ( TREM2 ) cerebrospinal fluid (CSF) concentrations and brain gene expression patterns. We found that [ 11 C]PBR28 correlated with CSF soluble TREM2 and showed regional distribution resembling TREM2 gene expression. Network analysis revealed that microglial activation and tau correlated hierarchically with each other following Braak-like stages. Regression analysis revealed that the longitudinal tau propagation pathways depended on the baseline microglia network rather than the tau network circuits. The co-occurrence of Aβ, tau and microglia abnormalities was the strongest predictor of cognitive impairment in our study population. Our findings support a model where an interaction between Aβ and activated microglia sets the pace for tau spread across Braak stages. Microglial activation and tau accumulation propagate together in patients with Alzheimer’s disease, suggesting an interaction that determines disease progression.

Background Liraglutide is a glucagon-like peptide-1 (GLP-1) analogue currently approved for type 2 diabetes and obesity. Preclinical evidence in transgenic models of Alzheimer’s disease suggests that liraglutide exerts neuroprotective effects by reducing amyloid oligomers, normalising synaptic plasticity and cerebral glucose uptake, and increasing the proliferation of neuronal progenitor cells. The primary objective of the study is to evaluate the change in cerebral glucose metabolic rate after 12 months of treatment with liraglutide in participants with Alzheimer’s disease compared to those who are receiving placebo. Methods/design ELAD is a 12-month, multi-centre, randomised, double-blind, placebo-controlled, phase IIb trial of liraglutide in participants with mild Alzheimer’s dementia. A total of 206 participants will be randomised to receive either liraglutide or placebo as a daily injection for a year. The primary outcome will be the change in cerebral glucose metabolic rate in the cortical regions (hippocampus, medial temporal lobe, and posterior cingulate) from baseline to follow-up in the treatment group compared with the placebo group. The key secondary outcomes are the change from baseline to 12 months in z scores for clinical and cognitive measures (Alzheimer’s Disease Assessment Scale—Cognitive Subscale and Executive domain scores of the Neuropsychological Test Battery, Clinical Dementia Rating Sum of Boxes, and Alzheimer’s Disease Cooperative Study—Activities of Daily Living) and the incidence and severity of treatment-emergent adverse events or clinically important changes in safety assessments. Other secondary outcomes are 12-month change in magnetic resonance imaging volume, diffusion tensor imaging parameters, reduction in microglial activation in a subgroup of participants, reduction in tau formation and change in amyloid levels in a subgroup of participants measured by tau and amyloid imaging, and changes in composite scores using support machine vector analysis in the treatment group compared with the placebo group. Discussion Alzheimer’s disease is a leading cause of morbidity worldwide. As available treatments are only symptomatic, the search for disease-modifying therapies is a priority. If the ELAD trial is successful, liraglutide and GLP-1 analogues will represent an important class of compounds to be further evaluated in clinical trials for Alzheimer’s treatment. Trial registration ClinicalTrials.gov, NCT01843075 . Registration 30 April 2013.

Alzheimer’s disease is the most common cause of dementia in the elderly population (aged 65 years and over), followed by vascular dementia, Lewy body dementia, and rare types of neurodegenerative diseases, including frontotemporal dementia. There is an unmet need to improve diagnosis and prognosis for patients with dementia, as cycles of misdiagnosis and diagnostic delays are challenging scenarios in neurodegenerative diseases. Neuroimaging is routinely used in clinical practice to support the diagnosis of neurodegenerative diseases. Clinical neuroimaging is amenable to errors owing to varying human judgement as the imaging data are complex and multidimensional. Artificial intelligence algorithms (machine learning and deep learning) enable automation of neuroimaging interpretation and may reduce potential bias and ameliorate clinical decision-making. Graph convolutional network-based frameworks implicitly provide multimodal sparse interpretability to support the detection of Alzheimer’s disease and its prodromal stage, mild cognitive impairment. In patients with amyloid-related imaging abnormalities, radiologists had significantly better detection performances with both ARIA-E (sensitivity higher in the assisted/deep learning method [87%] compared to unassisted [71%]) and for ARIA-H signs (sensitivity was higher in assisted [79%] compared to unassisted [69%]). A convolutional neural network method was developed, and external validation predicted final clinical diagnoses of Alzheimer’s disease, dementia with Lewy bodies, mild cognitive impairment due to Alzheimer’s disease, or cognitively normal with FDG-PET. The translation of artificial intelligence to clinical practice is plagued with technical, disease-related, and institutional challenges. The implementation of artificial intelligence methods in clinical practice has the potential to transform the diagnostic and treatment landscape and improve patient health and outcomes.

ObjectiveTo assess the overall pooled correlation coefficient estimate between cerebrospinal fluid (CSF) and blood neurofilament light (NfL) protein.MethodsWe searched Medline, Embase and Web of Science for published articles, from their inception to 9 July 2019, according to Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Studies reporting the correlation between CSF and blood NfL in humans were included. We conducted a random-effects meta-analysis to calculate the overall pooled correlation coefficient estimate, accounting for correlation technique and assay used. Heterogeneity was assessed using the I2 statistic test. In sensitivity analyses, we calculated the pooled correlation coefficient estimate according to blood NfL assay: single-molecule array digital immunoassay (Simoa), electrochemiluminescence (ECL) assay or ELISA.ResultsData were extracted from 36 articles, including 3961 paired CSF and blood NfL samples. Overall, 26/36 studies measured blood NfL using Simoa, 8/36 ECL, 1/36 ELISA and 1 study reported all three assay results. The overall meta-analysis demonstrated that the pooled correlation coefficient estimate for CSF and blood NfL was r=0.72. Heterogeneity was significant: I2=83%, p<0.01. In sensitivity analyses, the pooled correlation coefficient was similar for studies measuring blood NfL using Simoa and ECL (r=0.69 and r=0.68, respectively) but weaker for ELISA (r=0.35).ConclusionModerate correlations are demonstrated between CSF and blood NfL, especially when blood NfL was measured using Simoa and ECL. Given its high analytical sensitivity, Simoa is the preferred assay for measuring NfL, especially at low or physiological concentrations, and this meta-analysis supports its use as the current most advanced surrogate measure of CSF NfL.PROSPERO registration numberCRD42019140469

Microglia can take on proinflammatory or anti-inflammatory phenotypes, but it is unclear how these phenotypes play out along the Alzheimer's disease (AD) continuum. The purpose was to assess regional variances of microglial activation in distinct stages of the disease, and the role of microglial responses on grey matter volume and mean diffusivity in different brain areas and on cognition across the course of AD. 48 subjects (23 AD patients, 14 mild cognitive impairment [MCI], and 11 healthy controls [HC]) underwent TSPO-PET, diffusion tensor imaging (DTI) and extensive neuropsychometric assessment. SPM (Statistical parametric mapping) analysis was conducted for single subject analysis. GM volume for each subject was derived from T1 volumetric MRI using the FreeSurfer pipeline, mean diffusivity (DTI), alongside voxel-based morphometric analysis. Using postmortem brain tissue from 26 AD subjects, we evaluated CD32a and CD163 anti-bodies, markers of proinflammatory and anti-inflammatory microglia, respectively. Voxel-wise analyses identified positive and negative clusters of associations for IRF90 with GM volume and mean diffusivity. The presence of pro- and anti-inflammatory phenotypes was confirmed in human postmortem brain, in the frontal, parietal and entorhinal cortices. In addition, higher TSPO, signal in temporal lobes associated with lower hippocampal volume and mean diffusivity, and higher TSPO, signal across the cortex, correlated with poorer performance on neuropsychometric tests. There may be protective and deleterious microglial phenotypes in an individual, region specific and disease stage dependent. Therapeutic modulation of microglia is warranted to promote anti-inflammatory functions while suppressing pro-inflammatory functions.