Anti-amyloid immunotherapies have recently emerged as treatments for Alzheimer’s disease. While these therapies have demonstrated efficacy in clearing amyloid-β and slowing cognitive decline, they have also been associated with amyloid-related imaging abnormalities (ARIA) which include both edema (ARIA-E) and hemorrhage (ARIA-H). Given that ARIA have been associated with significant morbidity in cases of antithrombotic or thrombolytic therapy, an understanding of mechanisms of and risk factors for ARIA is of critical importance for stroke care. We discuss the latest data regarding mechanisms of ARIA, including the role of underlying cerebral amyloid angiopathy, and implications for ischemic stroke prevention and management.

Background: Electronic health records (EHRs) with large sample sizes and rich information offer great potential for dementia research, but current methods of phenotyping cognitive status are not scalable. Objective: The aim of this study was to evaluate whether natural language processing (NLP)–powered semiautomated annotation can improve the speed and interrater reliability of chart reviews for phenotyping cognitive status. Methods: In this diagnostic study, we developed and evaluated a semiautomated NLP-powered annotation tool (NAT) to facilitate phenotyping of cognitive status. Clinical experts adjudicated the cognitive status of 627 patients at Mass General Brigham (MGB) health care, using NAT or traditional chart reviews. Patient charts contained EHR data from two data sets: (1) records from January 1, 2017, to December 31, 2018, for 100 Medicare beneficiaries from the MGB Accountable Care Organization and (2) records from 2 years prior to COVID-19 diagnosis to the date of COVID-19 diagnosis for 527 MGB patients. All EHR data from the relevant period were extracted; diagnosis codes, medications, and laboratory test values were processed and summarized; clinical notes were processed through an NLP pipeline; and a web tool was developed to present an integrated view of all data. Cognitive status was rated as cognitively normal, cognitively impaired, or undetermined. Assessment time and interrater agreement of NAT compared to manual chart reviews for cognitive status phenotyping was evaluated. Results: NAT adjudication provided higher interrater agreement (Cohen κ=0.89 vs κ=0.80) and significant speed up (time difference mean 1.4, SD 1.3 minutes; P<.001; ratio median 2.2, min-max 0.4-20) over manual chart reviews. There was moderate agreement with manual chart reviews (Cohen κ=0.67). In the cases that exhibited disagreement with manual chart reviews, NAT adjudication was able to produce assessments that had broader clinical consensus due to its integrated view of highlighted relevant information and semiautomated NLP features. Conclusions: NAT adjudication improves the speed and interrater reliability for phenotyping cognitive status compared to manual chart reviews. This study underscores the potential of an NLP-based clinically adjudicated method to build large-scale dementia research cohorts from EHRs.

Nucleoporin 98 is a nuclear pore complex component that is mislocalized in Alzheimer’s disease and the alteration in nucleoporin 98 has been attributed to tau. In order to determine if nucleoporin 98 mislocalization is a general feature of tauopathies, we assessed the localization of nucleoporin 98 in neurons in primary tauopathies, including frontotemporal lobar degeneration-tau, corticobasal degeneration and progressive supranuclear palsy. Immunofluorescence staining was performed on frontal cortex and occipital cortex tissue from cases of primary tauopathies and controls without neurodegenerative disease using antibodies to identify nucleoporin 98, phospho-tau (Ser202, Thr205) monoclonal antibody and neuronal marker microtubule-associated protein 2. The stained tissue was imaged by fluorescence microscopy and the number of neurons with mislocalized nucleoporin 98 and phospho-tau (Ser202, Thr205) monoclonal antibody staining was quantified. In frontal cortex tissue, all primary tauopathies examined demonstrated significantly increased numbers of neurons with abnormal localization of nucleoporin 98 along the nuclear envelope compared with control tissue. Additionally, frontotemporal lobar degeneration-tau and corticobasal degeneration in the frontal cortex demonstrated significantly increased numbers of neurons with a cytoplasmic mislocalization of nucleoporin 98 compared with control tissue. The number of neurons with mislocalized nucleoporin 98 was significantly correlated with the number of neurons with phospho-tau (Ser202, Thr205) monoclonal antibody-positive tau staining. In the occipital cortex, which is relatively spared from pathological tau accumulations in these primary tauopathies, the localization of nucleoporin 98 was not significantly altered. This study demonstrates that nucleoporin 98 mislocalization is a feature of primary tauopathies and is associated with pathological tau accumulation. In the context of prior research demonstrating nucleoporin 98 mislocalization in Alzheimer’s disease and an interaction between tau and nucleoporin 98, these results further support the hypothesis that pathological tau may contribute to nucleoporin 98 mislocalization. Given the critical role of the nuclear pore complex in nucleocytoplasmic transport, the identification of nucleoporin 98 mislocalization in primary tauopathies highlights a potential pathophysiological disruption in these disorders.

Background: Alzheimer disease and related dementias (ADRD) exhibit prominent heterogeneity. Identifying clinically meaningful ADRD subtypes is essential for tailoring treatments to specific patient phenotypes. Objective: We aimed to use unsupervised learning techniques on electronic health records (EHRs) from memory clinic patients to identify ADRD subtypes. Methods: We used pretrained embeddings of non-ADRD diagnosis codes (International Classification of Diseases, Ninth Revision) and large language model (LLM)–derived embeddings of clinical notes from patient EHRs. Hierarchical clustering of these embeddings was used to identify ADRD subtypes. Clusters were characterized regarding their demographic and clinical features. Results: We analyzed a cohort of 3454 patients with ADRD from a memory clinic at Massachusetts General Hospital, each with a specialist diagnosis. Clustering pretrained embeddings of the non-ADRD diagnosis codes in patient EHRs revealed the following 3 patient subtypes: one with skin conditions, another with psychiatric disorders and an earlier age of onset, and a third with diabetes complications. Similarly, using LLM-derived embeddings of clinical notes, we identified 3 subtypes of patients as follows: one with psychiatric manifestations and higher prevalence of female participants (prevalence ratio: 1.59), another with cardiovascular and motor problems and higher prevalence of male participants (prevalence ratio: 1.75), and a third one with geriatric health disorders. Notably, we observed significant overlap between clusters from both data modalities (χ24=89.4; P<.001). Conclusions: By integrating International Classification of Diseases, Ninth Revision codes and LLM-derived embeddings, our analysis delineated 2 distinct ADRD subtypes with sex-specific comorbid and clinical presentations, offering insights for potential precision medicine approaches.

Background: Alzheimer disease and related dementias (ADRD) are complex disorders with overlapping symptoms and pathologies. Comprehensive records of symptoms in electronic health records (EHRs) are critical for not only reaching an accurate diagnosis but also supporting ongoing research studies and clinical trials. However, these symptoms are frequently obscured within unstructured clinical notes in EHRs, making manual extraction both time-consuming and labor-intensive. Objective: We aimed to automate symptom extraction from the clinical notes of patients with ADRD using fine-tuned large language models (LLMs), compare its performance to regular expression-based symptom recognition, and validate the results using brain magnetic resonance imaging (MRI) data. Methods: We fine-tuned LLMs to extract ADRD symptoms across the following 7 domains: memory, executive function, motor, language, visuospatial, neuropsychiatric, and sleep. We assessed the algorithm’s performance by calculating the area under the receiver operating characteristic curve (AUROC) for each domain. The extracted symptoms were then validated in two analyses: (1) predicting ADRD diagnosis using the counts of extracted symptoms and (2) examining the association between ADRD symptoms and MRI-derived brain volumes. Results: Symptom extraction across the 7 domains achieved high accuracy with AUROCs ranging from 0.97 to 0.99. Using the counts of extracted symptoms to predict ADRD diagnosis yielded an AUROC of 0.83 (95% CI 0.77-0.89). Symptom associations with brain volumes revealed that a smaller hippocampal volume was linked to memory impairments (odds ratio 0.62, 95% CI 0.46-0.84; P=.006), and reduced pallidum size was associated with motor impairments (odds ratio 0.73, 95% CI 0.58-0.90; P=.04). Conclusions: These results highlight the accuracy and reliability of our high-throughput ADRD phenotyping algorithm. By enabling automated symptom extraction, our approach has the potential to assist with differential diagnosis, as well as facilitate clinical trials and research studies of dementia.

As Alzheimer disease (AD) progresses, pathological tau spreads by cell-to-cell propagation of tau. This study aims to elucidate the impact of AD-associated post-translational modifications of tau-on-tau propagation. Tau propagation reporter constructs distinguishing donor cells from recipient cells were developed, and additional constructs were made with tau residues mutated from serine or threonine to aspartate to mimic the negative charge of a phosphorylation and/or from lysine to glutamine to mimic the charge-neutralizing effect of acetylation. Flow cytometry was used to quantify donor and recipient cells. This revealed that the mutations generally tended to reduce tau propagation compared to wildtype tau. Recombinant tau containing either wildtype or posttranslational modification mimicking mutations were used to treat Chinese hamster ovary cells or human induced pluripotent stem cell-derived neurons to quantify tau uptake, revealing that the mutations generally resulted in reduced uptake compared to wildtype tau. Surface plasmon resonance revealed that the mutations had a reduced affinity for lipoprotein receptor-related protein 1 (LRP1), a tau uptake receptor, compared to wildtype tau. Overall, these results suggest that AD-associated posttranslational modification mimicking mutations reduce the cell-to-cell propagation of tau by reducing tau uptake by recipient cells, which may be in part due to reduced binding affinity to LRP1.