Landscape of brain myeloid cell transcriptome along the spatiotemporal progression of Alzheimer's disease reveals distinct sequential responses to Aß and tau.

Human microglia are critically involved in Alzheimer's disease (AD) progression, as shown by genetic and molecular studies. However, their role in tau pathology progression in human brain has not been well described. Here, we characterized 32 human donors along progression of AD pathology, both in time-from early to late pathology-and in space-from entorhinal cortex (EC), inferior temporal gyrus (ITG), prefrontal cortex (PFC) to visual cortex (V2 and V1)-with biochemistry, immunohistochemistry, and single nuclei-RNA-sequencing, profiling a total of 337,512 brain myeloid cells, including microglia. While the majority of microglia are similar across brain regions, we identified a specific subset unique to EC which may contribute to the early tau pathology present in this region. We calculated conversion of microglia subtypes to diseased states and compared conversion patterns to those from AD animal models. Targeting genes implicated in this conversion, or their upstream/downstream pathways, could halt gene programs initiated by early tau progression. We used expression patterns of early tau progression to identify genes whose expression is reversed along spreading of spatial tau pathology (EC > ITG > PFC > V2 > V1) and identified their potential involvement in microglia subtype conversion to a diseased state. This study provides a data resource that builds on our knowledge of myeloid cell contribution to AD by defining the heterogeneity of microglia and brain macrophages during both temporal and regional pathology aspects of AD progression at an unprecedented resolution.

Cryopreservation of cerebrospinal fluid cells preserves the transcriptional landscape for single-cell analysis.

Cerebrospinal fluid (CSF) matrix biomarkers have become increasingly valuable surrogate markers of neuropsychiatric diseases in research and clinical practice. In contrast, CSF cells have been rarely investigated due to their relative scarcity and fragility, and lack of common collection and cryopreservation protocols, with limited exceptions for neurooncology and primary immune-based diseases like multiple sclerosis. the advent of a microfluidics-based multi-omics approach to studying individual cells has allowed for the study of cellular phenotyping, intracellular dynamics, and intercellular relationships that provide multidimensionality unable to be obtained through acellular fluid-phase analyses. challenges to cell-based research include site-to-site differences in handling, storage, and thawing methods, which can lead to inaccuracy and inter-assay variability. In the present study, we performed single-cell RNA sequencing (10x Genomics) on fresh or previously cryopreserved human CSF samples from three alternative cryopreservation methods: Fetal Bovine Serum with Dimethyl sulfoxide (FBS/DMSO), FBS/DMSO after a DNase step (a step often included in epigenetic studies), and cryopreservation using commercially available Recovery© media. In comparing relative differences between fresh and cryopreserved samples, we found little effect of the cryopreservation method on being able to resolve donor-linked cell type proportions, markers of cellular stress, and overall gene expression at the single-cell level, whereas donor-specific differences were readily discernable. We further demonstrate the compatibility of fresh and cryopreserved CSF immune cell sequencing using biologically relevant sexually dimorphic gene expression differences by donor. Our findings support the utility and interchangeability of FBS/DMSO and Recovery cryopreservation with fresh sample analysis, providing a methodological grounding that will enable researchers to further expand our understanding of the CSF immune cell contributions to neurological and psychiatric disease.

Relapse to cocaine seeking is regulated by medial habenula NR4A2/NURR1 in mice.

Drugs of abuse can persistently change the reward circuit in ways that contribute to relapse behavior, partly via mechanisms that regulate chromatin structure and function. Nuclear orphan receptor subfamily4 groupA member2 (NR4A2, also known as NURR1) is an important effector of histone deacetylase 3 (HDAC3)-dependent mechanisms in persistent memory processes and is highly expressed in the medial habenula (MHb), a region that regulates nicotine-associated behaviors. Here, expressing the Nr4a2 dominant negative (Nurr2c) in the MHb blocks reinstatement of cocaine seeking in mice. We use single-nucleus transcriptomics to characterize the molecular cascade following Nr4a2 manipulation, revealing changes in transcriptional networks related to addiction, neuroplasticity, and GABAergic and glutamatergic signaling. The network controlled by NR4A2 is characterized using a transcription factor regulatory network inference algorithm. These results identify the MHb as a pivotal regulator of relapse behavior and demonstrate the importance of NR4A2 as a key mechanism driving the MHb component of relapse.

Alzheimer DataLENS: An Open Data Analytics Portal for Alzheimer's Disease Research.

BACKGROUND: Recent Alzheimer's disease (AD) discoveries are increasingly based on studies from a variety of omics technologies on large cohorts. Currently, there is no easily accessible resource for neuroscientists to browse, query, and visualize these complex datasets in a harmonized manner. OBJECTIVE: Create an online portal of public omics datasets for AD research. METHODS: We developed Alzheimer DataLENS, a web-based portal, using the R Shiny platform to query and visualize publicly available transcriptomics and genetics studies of AD on human cohorts. To ensure consistent representation of AD findings, all datasets were processed through a uniform bioinformatics pipeline. RESULTS: Alzheimer DataLENS currently houses 2 single-nucleus RNA sequencing datasets, over 30 bulk RNA sequencing datasets from 19 brain regions and 3 cohorts, and 2 genome-wide association studies (GWAS). Available visualizations for single-nucleus data include bubble plots, heatmaps, and UMAP plots; for bulk expression data include box plots and heatmaps; for pathways include protein-protein interaction network plots; and for GWAS results include Manhattan plots. Alzheimer DataLENS also links to two other knowledge resources: the AD Progression Atlas and the Astrocyte Atlas. CONCLUSIONS: Alzheimer DataLENS is a valuable resource for investigators to quickly and systematically explore omics datasets and is freely accessible at https://alzdatalens.partners.org.

Real-time imaging of mitochondrial redox reveals increased mitochondrial oxidative stress associated with amyloid ß aggregates in vivo in a mouse model of Alzheimer's disease.

BACKGROUND: Reactive oxidative stress is a critical player in the amyloid beta (Aß) toxicity that contributes to neurodegeneration in Alzheimer's disease (AD). Damaged mitochondria are one of the main sources of reactive oxygen species and accumulate in Aß plaque-associated dystrophic neurites in the AD brain. Although Aß causes neuronal mitochondria reactive oxidative stress in vitro, this has never been directly observed in vivo in the living mouse brain. Here, we tested for the first time whether Aß plaques and soluble Aß oligomers induce mitochondrial oxidative stress in surrounding neurons in vivo, and whether this neurotoxic effect can be abrogated using mitochondrial-targeted antioxidants. METHODS: We expressed a genetically encoded fluorescent ratiometric mitochondria-targeted reporter of oxidative stress in mouse models of the disease and performed intravital multiphoton microscopy of neuronal mitochondria and Aß plaques. RESULTS: For the first time, we demonstrated by direct observation in the living mouse brain exacerbated mitochondrial oxidative stress in neurons after both Aß plaque deposition and direct application of soluble oligomeric Aß onto the brain, and determined the most likely pathological sequence of events leading to oxidative stress in vivo. Oxidative stress could be inhibited by both blocking calcium influx into mitochondria and treating with the mitochondria-targeted antioxidant SS31. Remarkably, the latter ameliorated plaque-associated dystrophic neurites without impacting Aß plaque burden. CONCLUSIONS: Considering these results, combination of mitochondria-targeted compounds with other anti-amyloid beta or anti-tau therapies hold promise as neuroprotective drugs for the prevention and/or treatment of AD.

Cryptic splicing of stathmin-2 and UNC13A mRNAs is a pathological hallmark of TDP-43-associated Alzheimer's disease.

Nuclear clearance and cytoplasmic accumulations of the RNA-binding protein TDP-43 are pathological hallmarks in almost all patients with amyotrophic lateral sclerosis (ALS) and up to 50% of patients with frontotemporal dementia (FTD) and Alzheimer's disease. In Alzheimer's disease, TDP-43 pathology is predominantly observed in the limbic system and correlates with cognitive decline and reduced hippocampal volume. Disruption of nuclear TDP-43 function leads to abnormal RNA splicing and incorporation of erroneous cryptic exons in numerous transcripts including Stathmin-2 (STMN2, also known as SCG10) and UNC13A, recently reported in tissues from patients with ALS and FTD. Here, we identify both STMN2 and UNC13A cryptic exons in Alzheimer's disease patients, that correlate with TDP-43 pathology burden, but not with amyloid-ß or tau deposits. We also demonstrate that processing of the STMN2 pre-mRNA is more sensitive to TDP-43 loss of function than UNC13A. In addition, full-length RNAs encoding STMN2 and UNC13A are suppressed in large RNA-seq datasets generated from Alzheimer's disease post-mortem brain tissue. Collectively, these results open exciting new avenues to use STMN2 and UNC13A as potential therapeutic targets in a broad range of neurodegenerative conditions with TDP-43 proteinopathy including Alzheimer's disease.

Identification of Novel Biomarkers for Alzheimer's Disease and Related Dementias Using Unbiased Plasma Proteomics.

Alzheimer's disease (AD) and related dementias (ADRD) is a complex disease with multiple pathophysiological drivers that determine clinical symptomology and disease progression. These diseases develop insidiously over time, through many pathways and disease mechanisms and continue to have a huge societal impact for affected individuals and their families. While emerging blood-based biomarkers, such as plasma p-tau181 and p-tau217, accurately detect Alzheimer neuropthology and are associated with faster cognitive decline, the full extension of plasma proteomic changes in ADRD remains unknown. Earlier detection and better classification of the different subtypes may provide opportunities for earlier, more targeted interventions, and perhaps a higher likelihood of successful therapeutic development. In this study, we aim to leverage unbiased mass spectrometry proteomics to identify novel, blood-based biomarkers associated with cognitive decline. 1,786 plasma samples from 1,005 patients were collected over 12 years from partcipants in the Massachusetts Alzheimer's Disease Research Center Longitudinal Cohort Study. Patient metadata includes demographics, final diagnoses, and clinical dementia rating (CDR) scores taken concurrently. The Proteograph™ Product Suite (Seer, Inc.) and liquid-chromatography mass-spectrometry (LC-MS) analysis were used to process the plasma samples in this cohort and generate unbiased proteomics data. Data-independent acquisition (DIA) mass spectrometry results yielded 36,259 peptides and 4,007 protein groups. Linear mixed effects models revealed 138 differentially abundant proteins between AD and healthy controls. Machine learning classification models for AD diagnosis identified potential candidate biomarkers including MBP, BGLAP, and APoD. Cox regression models were created to determine the association of proteins with disease progression and suggest CLNS1A, CRISPLD2, and GOLPH3 as targets of further investigation as potential biomarkers. The Proteograph workflow provided deep, unbiased coverage of the plasma proteome at a speed that enabled a cohort study of almost 1,800 samples, which is the largest, deep, unbiased proteomics study of ADRD conducted to date.

Distinct transcriptomic responses to Aß plaques, neurofibrillary tangles, and APOE in Alzheimer's disease.

INTRODUCTION: Omics studies have revealed that various brain cell types undergo profound molecular changes in Alzheimer's disease (AD) but the spatial relationships with plaques and tangles and APOE-linked differences remain unclear. METHODS: We performed laser capture microdissection of amyloid beta (Aß) plaques, the 50 µm halo around them, tangles with the 50 µm halo around them, and areas distant (> 50 µm) from plaques and tangles in the temporal cortex of AD and control donors, followed by RNA-sequencing. RESULTS: Aß plaques exhibited upregulated microglial (neuroinflammation/phagocytosis) and downregulated neuronal (neurotransmission/energy metabolism) genes, whereas tangles had mostly downregulated neuronal genes. Aß plaques had more differentially expressed genes than tangles. We identified a gradient Aß plaque > peri-plaque > tangle > distant for these changes. AD APOE ε4 homozygotes had greater changes than APOE ε3 across locations, especially within Aß plaques. DISCUSSION: Transcriptomic changes in AD consist primarily of neuroinflammation and neuronal dysfunction, are spatially associated mainly with Aß plaques, and are exacerbated by the APOE ε4 allele.

Effect of Metformin on Plasma and Cerebrospinal Fluid Biomarkers in Non-Diabetic Older Adults with Mild Cognitive Impairment Related to Alzheimer's Disease.

BACKGROUND: Alzheimer's disease (AD) is a complicated condition involving multiple metabolic and immunologic pathophysiological processes that can occur with the hallmark pathologies of amyloid-ß, tau, and neurodegeneration. Metformin, an anti-diabetes drug, targets several of these disease processes in in vitro and animal studies. However, the effects of metformin on human cerebrospinal fluid (CSF) and plasma proteins as potential biomarkers of treatment remain unexplored. OBJECTIVE: Using proteomics data from a metformin clinical trial, identify the impact of metformin on plasma and CSF proteins. METHODS: We analyzed plasma and CSF proteomics data collected previously (ClinicalTrials.gov identifier: NCT01965756, conducted between 2013 and 2015), and conduced bioinformatics analyses to compare the plasma and CSF protein levels after 8 weeks of metformin or placebo use to their baseline levels in 20 non-diabetic patients with mild cognitive impairment (MCI) and positive AD biomarkers participants. RESULTS: 50 proteins were significantly (unadjusted p <  0.05) altered in plasma and 26 in CSF after 8 weeks of metformin use, with 7 proteins in common (AZU1, CASP-3, CCL11, CCL20, IL32, PRTN3, and REG1A). The correlation between changes in plasma and CSF levels of these 7 proteins after metformin use relative to baseline levels was high (r = 0.98). The proteins also demonstrated temporal stability. CONCLUSIONS: Our pilot study is the first to investigate the effect of metformin on plasma and CSF proteins in non-diabetic patients with MCI and positive AD biomarkers and identifies several candidate plasma biomarkers for future clinical trials after confirmatory studies.

An aspect-level sentiment analysis dataset for therapies on Twitter.

The dataset described is an aspect-level sentiment analysis dataset for therapies, including medication, behavioral and other therapies, created by leveraging user-generated text from Twitter. The dataset was constructed by collecting Twitter posts using keywords associated with the therapies (often referred to as treatments). Subsequently, subsets of the collected posts were manually reviewed, and annotation guidelines were developed to categorize the posts as positive, negative, or neutral. The dataset contains a total of 5364 posts mentioning 32 therapies. These posts are further categorized manually into 998 (18.6%) positive, 619 (11.5%) negatives, and 3747 (69.9%) neutral sentiments. The inter-annotation agreement for the dataset was evaluated using Cohen's Kappa score, achieving an 0.82 score. The potential use of this dataset lies in the development of automatic systems that can detect users' sentiments toward therapies based on their posts. While there are other sentiment analysis datasets available, this is the first that encodes sentiments associated with specific therapies. Researchers and developers can utilize this dataset to train sentiment analysis models, natural language processing algorithms, or machine learning systems to accurately identify and analyze the sentiments expressed by consumers on social media platforms like Twitter.

causalCmprsk: An R package for nonparametric and Cox-based estimation of average treatment effects in competing risks data.

BACKGROUND AND OBJECTIVE: Competing risks data arise in both observational and experimental clinical studies with time-to-event outcomes, when each patient might follow one of the multiple mutually exclusive competing paths. Ignoring competing risks in the analysis can result in biased conclusions. In addition, possible confounding bias of the treatment-outcome relationship has to be addressed, when estimating treatment effects from observational data. In order to provide tools for estimation of average treatment effects on time-to-event outcomes in the presence of competing risks, we developed the R package causalCmprsk. We illustrate the package functionality in the estimation of effects of a right heart catheterization procedure on discharge and in-hospital death from observational data. METHODS: The causalCmprsk package implements an inverse probability weighting estimation approach, aiming to emulate baseline randomization and alleviate possible treatment selection bias. The package allows for different types of weights, representing different target populations. causalCmprsk builds on existing methods from survival analysis and adapts them to the causal analysis in non-parametric and semi-parametric frameworks. RESULTS: The causalCmprsk package has two main functions: fit.cox assumes a semiparametric structural Cox proportional hazards model for the counterfactual cause-specific hazards, while fit.nonpar does not impose any structural assumptions. In both frameworks, causalCmprsk implements estimators of (i) absolute risks for each treatment arm, e.g., cumulative hazards or cumulative incidence functions, and (ii) relative treatment effects, e.g., hazard ratios, or restricted mean time differences. The latter treatment effect measure translates the treatment effect from probability into more intuitive time domain and allows the user to quantify, for example, by how many days or months the treatment accelerates the recovery or postpones illness or death. CONCLUSIONS: The causalCmprsk package provides a convenient and useful tool for causal analysis of competing risks data. It allows the user to distinguish between different causes of the end of follow-up and provides several time-varying measures of treatment effects. The package is accompanied by a vignette that contains more details, examples and code, making the package accessible even for non-expert users.

Spatial and single-nucleus transcriptomic analysis of genetic and sporadic forms of Alzheimer's Disease.

The pathogenesis of Alzheimer's disease (AD) depends on environmental and heritable factors, with remarkable differences evident between individuals at the molecular level. Here we present a transcriptomic survey of AD using spatial transcriptomics (ST) and single-nucleus RNA-seq in cortical samples from early-stage AD, late-stage AD, and AD in Down Syndrome (AD in DS) donors. Studying AD in DS provides an opportunity to enhance our understanding of the AD transcriptome, potentially bridging the gap between genetic mouse models and sporadic AD. Our analysis revealed spatial and cell-type specific changes in disease, with broad similarities in these changes between sAD and AD in DS. We performed additional ST experiments in a disease timecourse of 5xFAD and wildtype mice to facilitate cross-species comparisons. Finally, amyloid plaque and fibril imaging in the same tissue samples used for ST enabled us to directly link changes in gene expression with accumulation and spread of pathology.

Ether lipid biosynthesis promotes lifespan extension and enables diverse pro-longevity paradigms in Caenorhabditis elegans.

Biguanides, including the world's most prescribed drug for type 2 diabetes, metformin, not only lower blood sugar, but also promote longevity in preclinical models. Epidemiologic studies in humans parallel these findings, indicating favorable effects of metformin on longevity and on reducing the incidence and morbidity associated with aging-related diseases. Despite this promise, the full spectrum of molecular effectors responsible for these health benefits remains elusive. Through unbiased screening in Caenorhabditis elegans, we uncovered a role for genes necessary for ether lipid biosynthesis in the favorable effects of biguanides. We demonstrate that biguanides prompt lifespan extension by stimulating ether lipid biogenesis. Loss of the ether lipid biosynthetic machinery also mitigates lifespan extension attributable to dietary restriction, target of rapamycin (TOR) inhibition, and mitochondrial electron transport chain inhibition. A possible mechanistic explanation for this finding is that ether lipids are required for activation of longevity-promoting, metabolic stress defenses downstream of the conserved transcription factor skn-1/Nrf. In alignment with these findings, overexpression of a single, key, ether lipid biosynthetic enzyme, fard-1/FAR1, is sufficient to promote lifespan extension. These findings illuminate the ether lipid biosynthetic machinery as a novel therapeutic target to promote healthy aging.

Metformin is the drug most prescribed to treat type 2 diabetes around the world and has been in clinical use since 1950. The drug belongs to a family of compounds known as biguanides which reduce blood sugar, making them an effective treatment against type 2 diabetes. More recently, biguanides have been found to have other health benefits, including limiting the growth of various cancer cells and improving the lifespan and long-term health of several model organisms. Epidemiologic studies also suggest that metformin may increase the lifespan of humans and reduce the incidence of age-related illnesses such as cardiovascular disease, cancer and dementia. Given the safety and effectiveness of metformin, understanding how it exerts these desirable effects may allow scientists to discover new mechanisms to promote healthy aging. The roundworm Caenorhabditis elegans is an ideal organism for studying the lifespan-extending effects of metformin. It has an average lifespan of two weeks, a genome that is relatively easy to manipulate, and a transparent body that enables scientists to observe cellular and molecular events in living worms. To discover the genes that enable metformin's lifespan-extending properties, Cedillo, Ahsan et al. systematically switched off the expression of about 1,000 genes involved in C. elegans metabolism. They then screened for genes which impaired the action of biguanides when inactivated. This ultimately led to the identification of a set of genes involved in promoting a longer lifespan. Cedillo, Ahsan et al. then evaluated how these genes impacted other well-described pathways involved in longevity and stress responses. The analysis indicated that a biguanide drug called phenformin (which is similar to metformin) increases the synthesis of ether lipids, a class of fats that are critical components of cellular membranes. Indeed, genetically mutating the three major enzymes required for ether lipid production stopped the biguanide from extending the worms' lifespans. Critically, inactivating these genes also prevented lifespan extension through other known strategies, such as dietary restriction and inhibiting the cellular organelle responsible for producing energy. Cedillo, Ahsan et al. also showed that increasing ether lipid production alters the activity of a well-known longevity and stress response factor called SKN-1, and this change alone is enough to extend the lifespan of worms. These findings suggest that promoting the production of ether lipids could lead to healthier aging. However, further studies, including clinical trials, will be required to determine whether this is a viable approach to promote longevity and health in humans.

Few-shot learning for medical text: A review of advances, trends, and opportunities.

BACKGROUND: Few-shot learning (FSL) is a class of machine learning methods that require small numbers of labeled instances for training. With many medical topics having limited annotated text-based data in practical settings, FSL-based natural language processing (NLP) holds substantial promise. We aimed to conduct a review to explore the current state of FSL methods for medical NLP. METHODS: We searched for articles published between January 2016 and October 2022 using PubMed/Medline, Embase, ACL Anthology, and IEEE Xplore Digital Library. We also searched the preprint servers (e.g., arXiv, medRxiv, and bioRxiv) via Google Scholar to identify the latest relevant methods. We included all articles that involved FSL and any form of medical text. We abstracted articles based on the data source, target task, training set size, primary method(s)/approach(es), and evaluation metric(s). RESULTS: Fifty-one articles met our inclusion criteria-all published after 2018, and most since 2020 (42/51; 82%). Concept extraction/named entity recognition was the most frequently addressed task (21/51; 41%), followed by text classification (16/51; 31%). Thirty-two (61%) articles reconstructed existing datasets to fit few-shot scenarios, and MIMIC-III was the most frequently used dataset (10/51; 20%). 77% of the articles attempted to incorporate prior knowledge to augment the small datasets available for training. Common methods included FSL with attention mechanisms (20/51; 39%), prototypical networks (11/51; 22%), meta-learning (7/51; 14%), and prompt-based learning methods, the latter being particularly popular since 2021. Benchmarking experiments demonstrated relative underperformance of FSL methods on biomedical NLP tasks. CONCLUSION: Despite the potential for FSL in biomedical NLP, progress has been limited. This may be attributed to the rarity of specialized data, lack of standardized evaluation criteria, and the underperformance of FSL methods on biomedical topics. The creation of publicly-available specialized datasets for biomedical FSL may aid method development by facilitating comparative analyses.

Age-dependent differences and similarities in the plasma proteomic signature of postoperative delirium.

Delirium is an acute confusional state and a common postoperative morbidity. Prevalent in older adults, delirium occurs at other ages but it is unclear whether the pathophysiology and biomarkers for the condition are independent of age. We quantified expression of 273 plasma proteins involved in inflammation and cardiovascular or neurologic conditions in 34 middle-aged and 42 older patients before and one day after elective spine surgery. Delirium was identified by the 3D-CAM and comprehensive chart review. Protein expression was measure by Proximity Extension Assay and results were analyzed by logistic regression, gene set enrichment, and protein-protein interactions. Twenty-two patients developed delirium postoperatively (14 older; 8 middle-aged) and 89 proteins in pre- or 1-day postoperative plasma were associated with delirium. A few proteins (IL-8, LTBR, TNF-R2 postoperatively; IL-8, IL-6, LIF, ASGR1 by pre- to postoperative change) and 12 networks were common to delirium in both age groups. However, there were marked differences in the delirium proteome by age; older patients had many more delirium-associated proteins and pathways than middle-aged subjects even though both had the same clinical syndrome. Therefore, there are age-dependent similarities and differences in the plasma proteomic signature of postoperative delirium, which may signify age differences in pathogenesis of the syndrome.

Cognitive concerns are a risk factor for mortality in people with HIV and coronavirus disease 2019.

BACKGROUND: Data supporting dementia as a risk factor for coronavirus disease 2019 (COVID-19) mortality relied on ICD-10 codes, yet nearly 40% of individuals with probable dementia lack a formal diagnosis. Dementia coding is not well established for people with HIV (PWH), and its reliance may affect risk assessment. METHODS: This retrospective cohort analysis of PWH with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PCR positivity includes comparisons to people without HIV (PWoH), matched by age, sex, race, and zipcode. Primary exposures were dementia diagnosis, by International Classification of Diseases (ICD)-10 codes, and cognitive concerns, defined as possible cognitive impairment up to 12 months before COVID-19 diagnosis after clinical review of notes from the electronic health record. Logistic regression models assessed the effect of dementia and cognitive concerns on odds of death [odds ratio (OR); 95% CI (95% confidence interval)]; models adjusted for VACS Index 2.0. RESULTS: Sixty-four PWH were identified out of 14 129 patients with SARS-CoV-2 infection and matched to 463 PWoH. Compared with PWoH, PWH had a higher prevalence of dementia (15.6% vs. 6%, P  = 0.01) and cognitive concerns (21.9% vs. 15.8%, P  = 0.04). Death was more frequent in PWH ( P  < 0.01). Adjusted for VACS Index 2.0, dementia [2.4 (1.0-5.8), P  = 0.05] and cognitive concerns [2.4 (1.1-5.3), P  = 0.03] were associated with increased odds of death. In PWH, the association between cognitive concern and death trended towards statistical significance [3.92 (0.81-20.19), P  = 0.09]; there was no association with dementia. CONCLUSION: Cognitive status assessments are important for care in COVID-19, especially among PWH. Larger studies should validate findings and determine long-term COVID-19 consequences in PWH with preexisting cognitive deficits.

Association of BCG Vaccine Treatment With Death and Dementia in Patients With Non-Muscle-Invasive Bladder Cancer.

Importance: The BCG vaccine-used worldwide to prevent tuberculosis-confers multiple nonspecific beneficial effects, and intravesical BCG vaccine is currently the recommended treatment for non-muscle-invasive bladder cancer (NMIBC). Moreover, BCG vaccine has been hypothesized to reduce the risk of Alzheimer disease and related dementias (ADRD), but previous studies have been limited by sample size, study design, or analyses. Objective: To evaluate whether intravesical BCG vaccine exposure is associated with a decreased incidence of ADRD in a cohort of patients with NMIBC while accounting for death as a competing event. Design, Setting, and Participants: This cohort study was performed in patients aged 50 years or older initially diagnosed with NMIBC between May 28, 1987, and May 6, 2021, treated within the Mass General Brigham health care system. The study included a 15-year follow-up of individuals (BCG vaccine treated or controls) whose condition did not clinically progress to muscle-invasive cancer within 8 weeks and did not have an ADRD diagnosis within the first year after the NMIBC diagnosis. Data analysis was conducted from April 18, 2021, to March 28, 2023. Main Outcomes and Measures: The main outcome was time to ADRD onset identified using diagnosis codes and medications. Cause-specific hazard ratios (HRs) were estimated using Cox proportional hazards regression after adjusting for confounders (age, sex, and Charlson Comorbidity Index) using inverse probability scores weighting. Results: In this cohort study including 6467 individuals initially diagnosed with NMIBC between 1987 and 2021, 3388 patients underwent BCG vaccine treatment (mean [SD] age, 69.89 [9.28] years; 2605 [76.9%] men) and 3079 served as controls (mean [SD] age, 70.73 [10.00] years; 2176 [70.7%] men). Treatment with BCG vaccine was associated with a lower rate of ADRD (HR, 0.80; 95% CI, 0.69-0.99), with an even lower rate of ADRD in patients aged 70 years or older at the time of BCG vaccine treatment (HR, 0.74; 95% CI, 0.60-0.91). In competing risks analysis, BCG vaccine was associated with a lower risk of ADRD (5-year risk difference, -0.011; 95% CI, -0.019 to -0.003) and a decreased risk of death in patients without an earlier diagnosis of ADRD (5-year risk difference, -0.056; 95% CI, -0.075 to -0.037). Conclusions and Relevance: In this study, BCG vaccine was associated with a significantly lower rate and risk of ADRD in a cohort of patients with bladder cancer when accounting for death as a competing event. However, the risk differences varied with time.

Endothelial Cells Are Heterogeneous in Different Brain Regions and Are Dramatically Altered in Alzheimer's Disease.

Vascular endothelial cells play an important role in maintaining brain health, but their contribution to Alzheimer's disease (AD) is obscured by limited understanding of the cellular heterogeneity in normal aged brain and in disease. To address this, we performed single nucleus RNAseq on tissue from 32 human AD and non-AD donors (19 female, 13 male) each with five cortical regions: entorhinal cortex, inferior temporal gyrus, prefrontal cortex, visual association cortex, and primary visual cortex. Analysis of 51,586 endothelial cells revealed unique gene expression patterns across the five regions in non-AD donors. Alzheimer's brain endothelial cells were characterized by upregulated protein folding genes and distinct transcriptomic differences in response to amyloid ß plaques and cerebral amyloid angiopathy. This dataset demonstrates previously unrecognized regional heterogeneity in the endothelial cell transcriptome in both aged non-AD and AD brain.SIGNIFICANCE STATEMENT In this work, we show that vascular endothelial cells collected from five different brain regions display surprising variability in gene expression. In the presence of Alzheimer's disease pathology, endothelial cell gene expression is dramatically altered with clear differences in regional and temporal changes. These findings help explain why certain brain regions appear to differ in susceptibility to disease-related vascular remodeling events that may impact blood flow.