bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of iPSC‐derived AD platforms for phenotypic screening
Authors: Poon Wayne , Zhou Peng , Murchison Angela , Noel Christopher , Haag Daniel , Wu Ji , Huang Tao 
Published: December 2022

AbstractBackgroundHuman iPSC‐derived in vitro neural co‐culture systems have been used increasingly in drug discovery for neurological and neurodegenerative disorders.MethodHere we describe the development of iPSC‐derived platforms of AD that can be used to screen for preclinical targets and drug candidates. The foundation of these platforms utilizes a proprietary method for the generation of pure populations of NGN2‐based excitatory and ASCL1/DLX2 inhibitory neurons.ResultTransgenic overexpression of tau isoforms leads to prion‐like tau spreading that can be used to screen for compounds to mitigate tau propagation. Using this platform, we investigated how tau overexpression leads to neuron‐neuron tau transmission, alters neuron electrophysiology as assessed by MEA, and leads to the accumulation of key neuropathological tau biomarkers. In addition, we have developed co‐culture platforms that facilitate examination of the role of neuroinflammation on the development of key neuropathological hallmarks of AD. This platform can be used in conjunction with isogenic iPSCs to interrogate the mechanism by which genetic risk factors confer increased or decreased AD risk.ConclusionTaken together, these phenotypic assays demonstrate the utility of NeuCyte platforms that can be applied to high‐throughput drug screening efforts for AD.
Modifiable dementia risk factors associated with higher CSF tau and poorer cognition in middle‐aged adults
Authors: Bransby Lisa , Rosenich Emily , Buckley Rachel F. , Maruff Paul , Yassi Nawaf , Lim Yen Ying 
Published: December 2022

AbstractBackgroundModifiable factors in domains such as mood, lifestyle behaviours, cardiovascular health, cognitive/social engagement, and sleep are associated with increased risk for dementia. These modifiable risk factors could promote Alzheimer’s disease (AD) related biological processes, such as beta‐amyloid (Aβ) or tau accumulation, but these associations remain poorly understood. This study aimed to determine associations between modifiable dementia risk factors with Aβ, tau, and cognition in cognitively unimpaired middle‐aged adults.MethodMiddle‐aged adults (n = 82) (age range 40‐70) enrolled in a biomarker sub‐study of the Healthy Brain Project completed self‐report questionnaires about their physical and psychological health and lifestyle. Cerebrospinal fluid (CSF) levels of Aβ42, total tau (t‐tau), and phosphorylated tau (p‐tau) (Roche Elecsys) were obtained. A comprehensive neuropsychological battery was administered to measure cognition, and composite scores were derived for memory, executive function, and the Preclinical Alzheimer’s Cognitive Composite (PACC). Participants were classified according to reporting normal modifiable risk (NMR) (risk in ≤1 domains; n = 34) or high modifiable risk (HMR) (risk in ≥2 domains; n = 48) across five domains (mood, lifestyle behaviours, cardiovascular health, cognitive and social engagement, and sleep).ResultCompared to those with NMR, those with HMR had increased t‐tau (d = 0.54, p = .021) and p‐tau (d = 0.47, p = .044) levels but did not differ on Aβ42 (d = 0.23, p = .319) after adjusting for age and sex. With age, sex, and education adjusted for, HMR participants showed worse performance on PACC (d = 0.69, p = .003), executive function (d = 0.55, p = .016), and episodic memory (d = 0.40, p = 0.076) compared to NMR participants. Differences in cognitive performance remained when levels of Aβ42, t‐tau and p‐tau levels were controlled in the models [PACC (d = 0.59, p = .011), memory (d = 0.64, p = .005) and executive function (d = 0.45, p = .049)].ConclusionModifiable dementia risk factors across multiple domains are related to higher total and phosphorylated tau levels and with subtle cognitive deficits, but not with Aβ, in middle‐aged adults. Together with the observation that differences in cognition across modifiable risk groups remained when Aβ and tau levels were controlled statistically, this suggests that modifiable dementia risk factors may increase risk through neurodegenerative processes non‐specific to AD, such as increased cerebrovascular burden, although this needs to be confirmed in future neuroimaging studies.
Impact of left censoring on longitudinal analyses of dementia using electronic health record data
Authors: Jiang Luohua , Shi Yuxi , Conniff Kyle R , Corrada Maria M. , Manson Spero , O'Connell Joan 
Published: December 2022

AbstractBackgroundLongitudinal electronic health record (EHR) data provides an opportunity to evaluate associations between certain risk factors and dementia incidence. In practice, researchers using EHR usually extract data for multiple years and use the first 2‐4 years as a washout period to exclude baseline prevalent cases. However, identifying dementia via clinical diagnostic codes during a washout period likely under‐detect some cases, with time to dementia diagnosis for these cases left censored and estimates for the associations of interest potentially biased. This study aims to assess the amount of left censoring when analyzing time to dementia using EHR data from the Indian Health Service (IHS).MethodsWe extracted data from the IHS National Data Warehouse and related EHR databases between fiscal year (FY) 2007‐2013. Adults aged 45+ years were identified as having dementia if they had at least one qualifying ICD‐9 diagnostic code for all‐cause dementia. A total of 1,117 AI/AN adults were identified as dementia patients in FY2007. Among these, we calculated the proportions of patients with 1+ ICD‐9 code for dementia within 1‐5 years after FY2007. We used diabetes, identified in a similar manner, as a comparison condition.ResultsAmong the FY2007 dementia patients who used IHS services in FY2008, only 63.7% had 1+ qualifying ICD‐9 code for dementia in FY2008. In contrast, 93.7% of FY2007 diabetes patients had an ICD‐9 code for diabetes in FY2008 (Fig1). If FY2007 data are not included in the analytical dataset and only one year of data (FY2008) is used as the washout period, the remaining 36.3% prevalent dementia cases are classified as incidence after FY2008 with their time to dementia left censored. Even using a 5‐year washout period, 25.0% prevalent dementia patients in FY2007 are classified as incident cases after FY2012 (Fig2a) while 4.6% prevalent diabetes patients in FY2007 are classified as incidence (Fig2b).ConclusionsLongitudinal analysis of dementia risk using EHR data might be subject to relatively high amount of left censoring. It is important to recognize the potential biases associated with relatively high proportion of left censored observations when estimating longitudinal associations of risk factors with dementia incidence.
A Cluster‐Based Machine Learning Approach for Evaluating Factors Associated with Tau Protein in Plasma
Authors: Hu Yi‐Han , Meirelles Osorio , Shrioma Eric , Satizabal Claudia L , Tracy Russell P. , Launer Lenore J. 
Published: December 2022

AbstractBackgroundPlasma total tau (t‐tau) has shown acceptable diagnostic accuracy for Alzheimer’s disease (AD), but other factors than levels of t‐tau in the brain may influence peripheral levels. While little is known about peripheral factors that may influence plasma levels of t‐tau, such factors may be similar to those associated with AD, such as cardiovascular risk factors, which are often correlated. We have developed a machine learning pipeline to identify individual and clusters of correlated variables that might explain a proportion of the variance of plasma t‐tau levels in a well‐phenotyped population‐based cohort.MethodWe studied 992 dementia‐free older adults (mean age 76 years, 44% men) in the Age, Gene/Environment Susceptibility‐Reykjavik Study. We used agglomerative hierarchical clustering along with principal component (PC) analysis to group factors with moderate‐to‐high similarity from 290 potential predictors including socio‐demographic, anthropometric, laboratory, cardiometabolic and lifestyle factors, diseases and medications. We then calculated the PCs of the clusters and examined their relationship to plasma t‐tau. We trained two nonlinear models using t‐tau as the dependent outcome variable ‐ the extreme gradient boosting model (GBM) and support vector machine (SVM) ̶ on 80% of data using a ten‐fold cross‐validation approach; the models were evaluated on the remaining 20% of data. Model performance was assessed using the root mean square error (RMSE) and correlation. SHapley Additive exPlanations (SHAP) was used to rank and interpret the contribution of the clusters to the best‐performed model.ResultWe identified 25 distinct clusters. The best performing model was a SVM model including age, sex and the first PC from all 25 clusters(Figure 1). Based on the SHAP values in this model, we found the top 10 ranked clusters associated with p‐tau(Figure 2): platelets(5 variables), body size(33), physical activity‐walk(6), and tobacco(9) were negatively associated with t‐tau level, while body fat(8), sex, cognitive function(27), inflammation(7), blood pressure(6), and red blood cells(11) were positively associated with t‐tau.ConclusionOur novel approach suggests additional factors explain some of the variance in peripheral tau levels in older adults. Additional research is needed to confirm these findings and investigate whether these factors modulate or mediate tau‐related pathology.
Reducing misdiagnosis of Alzheimer’s Disease pathology utilizing CSF and amyloid PET
Authors: Esquivel Rianne N , De Simone Francesca , Benina Natalya , Gannon Sara , Bastard Nathalie Le , Calabro Amanda , Hawkins Douglas M , Vandijck Manu , Latham Jessica , Dickson Diana , Radwan Rachel R. 
Published: December 2022

AbstractBackgroundUtilizing cognitive tests alone, including the Mini‐Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA), cannot detect the presence of amyloid plaques and tangles in Alzheimer’s disease (AD). While in late stages of AD clinical diagnosis relying primarily on cognitive testing is correct 70‐90% of the time, in MCI accuracy is reduced to 50‐60%. Although autopsy is the gold standard for AD diagnosis, amyloid PET imaging and more recently the Lumipulse Gβ‐Amyloid Ratio (Aβ1‐42/Aβ1‐40) are validated to determine amyloid pathology. With the upcoming availability of disease modifying therapies targeting amyloid in MCI and early AD, and anti‐tau drugs in the pipeline, it is necessary to have true measures of AD pathology for a clinical evaluation early in the disease process. Here we examine the performance of cognitive testing alone for identification of amyloid positivity in MCI patients from the ADNI study when compared to amyloid PET and CSF testing.MethodA subset of 170 CSF samples from the ADNI biobank (all MCI; ≥ 50 years old; amyloid PET negative (n=74) or amyloid PET positive (n=96)) was used to examine correlation of PET, CSF and MMSE/MoCA scores. Amyloid PET images were derived from an FDA approved tracer (Florbetapir F18) and method. CSF Aβ1‐42 and Aβ1‐40 were measured using the LUMIPULSE G1200 (Fujirebio).ResultAt a set cutoff of 0.058, Aβ1‐42/Aβ1‐40 demonstrated a PPA and NPA of 85.9% and 93.5% respectively for identifying amyloid PET status in individuals suffering from cognitive complaints. When examining the Aβ1‐42/Aβ1‐40 ratio of the MCI patients compared to amyloid PET in ADNI, Aβ1‐42/Aβ1‐40 achieved an AUC of 0.90. Alternatively, the MMSE score and MOCA score had AUCs with amyloid PET of 0.61 and 0.63 respectively and exhibited poor correlation with CSF.ConclusionCSF measurements or amyloid PET provide greater accuracy in determining amyloid status than cognitive screening tests. MMSE or MoCA as standalone tests give low confidence in a correct pathological diagnosis for MCI patients. Moving forward it will be important to consider PET or CSF results to determine underlying pathology in patients with MCI to ensure proper treatments and avoid unwanted side effects.
Population‐based Discovery and Ethnically Mixed Mendelian Randomization Validation Identifies Telmisartan as a Candidate Drug for Alzheimer’s Disease in African Americans
Authors: Cheng Feixiong , Zhang Pengyue , Hou Yuan , Pieper Andrew A. , Leverenz James B. , Cummings Jeffrey 
Published: December 2022

AbstractBackgroundElevated incidences of Alzheimer’s disease (AD) and disease progression may be associated with African American (AA)‐specific disease comorbidities, such as hypertension, diabetes, and chronic kidney disease. Treatments and management of these comorbidities — such as antihypertensive angiotensin‐II receptor blockers (ARBs) — may reduce risk of AD in AA people, while conferring additional beneficial effects on renal function and diabetes control.MethodWe conducted a race‐specific pharmacoepidemiologic study to investigate the association between telmisartan (an ARB having PPAR‐gamma (PPARG) agonistic properties and beneficial anti‐diabetic effects) use and AD using Cox analysis, Kaplan‐Meier analysis, and propensity score‐matched log‐rank test. We further conducted ethnically mixed Mendelian randomization analysis to identify likely causal relationships between telmisartan use and AD in AA people. All models were adjusted for age, sex, underlying health, and comorbidities.ResultIn Cox analyses of ARB users in insurance claim data, telmisartan use was significantly associated with reduced risks of AD in AA people (Hazard ratio [HR] = 0.77, P‐value = 0.002), but was not associated with AD reduction in non‐Hispanic White Americans (HR = 0.97, P‐value = 0.411). Compared to lisinopril (an angiotensin‐converting‐enzyme inhibitor) use, telmisartan use was associated with stronger reduced risk of AD in AA people (HR = 0.68, P‐value < 0.001). In all sensitivity and secondary analyses, telmisartan use was associated with stronger protective effects (including AD and dementia) in AA people. Using ethnically mixed Mendelian randomization analysis from large genome‐wide association studies (over 2 million individuals) across AD, hypertension, and diabetes, we demonstrated that telmisartan has greater beneficial effect on AD in AA people compared to European populations.ConclusionWe identified that telmisartan is a candidate drug for potential prevention and treatment of AD in AA people, but not in non‐Hispanic White Americans. Our findings indicate that early comorbidity management (hypertension, diabetes, and kidney dysfunction) by telmisartan will significantly reduce AD incidence for aged individuals, in particular for aged AA people. The association between telmisartan use and decreased incidence of AD in AAs will require a randomized controlled trial with an ethnically diverse population to establish causality and explore therapeutic outcomes.
A Network‐based Deep Learning Framework Translates GWAS and Multi‐Omics Findings to Pathobiology and Drug Repurposing for Alzheimer’s Disease
Authors: Xu Jielin , Hou Yuan , Zhou Yadi , Bekris Lynn M. , Pieper Andrew A. , Cummings Jeffrey , Leverenz James B. , Cheng Feixiong 
Published: December 2022

AbstractBackgroundHuman genome sequencing studies have identified numerous loci associated with complex diseases, including Alzheimer’s disease (AD). However, translating human genetic and genomic findings (i.e., genome‐wide association studies [GWAS]) to pathobiology and therapeutic discovery remains a major challenge. Today, artificial intelligence and deep learning approaches that identify new risk genes and drug targets from human genome sequencing findings are enabling a more complete mechanistic understanding of disease biology. This is facilitating more rapid development of targeted therapeutic interventions for AD.MethodWe presented a network topology‐based deep learning framework to identify disease‐associated genes (NETTAG). NETTAG integrates multi‐genomics data and the human protein‐protein interactome to infer putative risk genes and drug targets impacted by GWAS loci. Specifically, we leveraged non‐coding GWAS loci effects on expression quantitative trait loci (eQTLs), histone‐QTLs, transcription factor binding‐QTLs, enhancers and CpG islands, promoter regions, open chromatin, and promoter flanking regions. The fundamental premise of NETTAG is that disease risk genes exhibit distinct functional characteristics compared to non‐risk genes, and can therefore be distinguished by their aggregated genomic features under the human protein interactome.ResultApplying NETTAG to the latest AD GWAS data, we identified 156 putative AD‐risk genes (i.e., APOE, BIN1, GSK3B, MARK4, and PICALM). We showed that predicted risk genes are: 1) significantly enriched in AD‐related pathobiological pathways, 2) more likely to be differentially expressed in transcriptomes and proteomes of AD brains, and 3) enriched in druggable targets with approved medicines. Specifically, we showed that NETTAG‐predicted genes (e.g., MEF2D, CPLX2, KLF4, ACTL6B, P2RX7 and etc.) are differentially expressed in AD‐associated microglia and astrocytes from single‐nuclei RNA‐sequencing data of human postmortem brains with varying degrees of AD neurobiology. Via network‐based prediction, we identified multiple repurposable drug candidates (i.e., choline, deferoxamine and ibudilast) for potential treatment of AD.ConclusionOur findings suggest that understanding human pathobiology and therapeutic development could benefit from network‐based deep learning methodology that utilizes GWAS findings with multimodal genomic analyses.
BAG3 attenuates tau hyperphosphorylation and gliosis induced by traumatic brain injury
Authors: Morrison Cody , Li Liangping , Liang Jiawen , Chen Shuo , Acosta Diana M , Fitzgerald Julie A , Karelina Kate , Bray Chelsea E , Weil Zachary M , Godbout Jonathan , Kokiko‐cochran Olga N , Saito Takashi , Saido Takaomi C. , Fu Hongjun 
Published: December 2022

AbstractBackgroundAlzheimer disease (AD) and traumatic brain injury (TBI) are two devastating brain disorders with complex relationships. Growing evidence supports that early or middle life of TBI may be a risk factor for developing late‐life AD and AD‐related dementias (ADRD). Tau hyperphosphorylation and gliosis may serve as a causative link between TBI and AD as well as ADRD. We have recently identified BCL2 associated athanogene 3 (BAG3) as a hub gene of regulating tau protein homeostasis. The protein level of BAG3 is significantly reduced in neurons, but it is increased in astrocytes of human AD compared to controls. We hypothesized that overexpression of BAG3 in neurons would attenuate tau hyperphosphorylation and gliosis induced by TBI.MethodWe injected 500 nL AAV9‐hSYN1‐eGFP‐2A‐hBAG3‐WPRE or AAV9‐hSYN1‐eGFP‐WPRE (control AAV9) into the hippocampal CA1 and DG regions of C57BL6/J and htau knock‐in mice. These mice were subjected to TBI surgery (controlled cortical impact, velocity: 3.00 m/s, depth: 0.8 mm, dwell time: 200 ms) three months post the injection. We then performed behavioral tests (open‐field test, Y‐maze, and Morris Water Maze) on these mice one month after the CCI surgery. Following the behavioral tests, we collected the brain tissues and performed the immunofluorescent staining of ptau (PHF1, pS396/404 tau), BAG3, IBA‐1 (microglia/macrophage marker) and GFAP (astrocyte marker) on fixed mouse brain floating sections.ResultTBI increased the immunoreactivity of PHF1, IBA‐1, GFAP and astrocytic BAG3, whereas reducing neuronal BAG3 compared to sham C57BL6/J and htau knock‐in mice. Furthermore, the immunoreactivity of BAG3 increased, while PHF1, IBA‐1 and GFAP decreased in the ipsilateral hippocampus after the injection of AAV9‐BAG3 compared to those TBI mice injected with control AAVs. Continued immunofluorescent staining and quantification for AAV9‐BAG3‐ and control AAV9‐injected mice is currently ongoing, as is behavior data quantification.ConclusionTBI induces tau hyperphosphorylation, microglia activation and reactive astrocytes, while reducing neuronal BAG3 in C57BL6/J and htau knock‐in mice. Neuronal overexpression of BAG3 can significantly attenuate tau hyperphosphorylation and gliosis induced by TBI. Our data suggests that targeting neuronal BAG3 may be a therapeutic strategy for preventing or reducing intra‐neuronal tau aggregates and gliosis found in TBI and TBI‐associated AD and ADRD.
Combined segmentation and classification of Alzheimer’s disease pathology distribution within human hippocampal subregions
Authors: Stephen Terri‐Leigh , Korobkova Laura , Jones Kymry T , Cabeen Ryan P , Pachicano Maricarmen , Hawes Debra , Miller Carol A , Bienkowski Michael S 
Published: December 2022

AbstractBackgroundAlzheimer’s disease (AD) is a complex neurodegenerative disease characterized by progressive amyloid and tau pathology. Quantifying and classifying AD pathology across the human brain is challenging due to its large size and expertise needed for critical evaluation of the morphological variety in structure types (e.g. diffuse vs dense plaques, neurofibrillary tangles, neuropil threads, etc.). High‐throughput microscopy imaging and artificial intelligence allow for large‐scale, quantitative ‘digital pathology’ surveys. Due to their structural differences, distinct pathological aggregates in microscopy images can be identified and quantified by trained artificial neural networks (Tang et al 2019). However, current approaches do not incorporate anatomical segmentation to map the distribution of AD pathology across whole tissue sections. Understanding where and when AD pathology is distributed across different brain regions is critical to defining cell type susceptibility to neurodegeneration. This project’s goal was to create a combined neural network classification and segmentation quantification approach to generate anatomically‐accurate pathology maps of the human hippocampus and investigate the spatial distribution of distinct types of pathology.MethodWhole slide images of 4G8 and Gallyas stained coronal tissue sections from the USC ADRC Neuropathology Core were imaged using a slide scanning microscope (29 patients with low, intermediate, or high AD stages). 4G8 sections were manually annotated and segmented using the Allen Human Brain Atlas into thirteen layered hippocampal subregions and delineated using Quantitative Imaging Toolkit (QIT). The machine learning toolkit Ilastik was utilized to quantify and classify 4G8 amyloid staining (dense vs diffuse plaques) and Gallyas (neurofibrillary tangles) within each hippocampal subregion and overlaid to segmentation maps.ResultIn the hippocampus, we found diffuse amyloid pathology increased more rapidly across progressive AD stages compared to dense amyloid plaques or tau neurofibrillary tangles. The ratio of diffuse/dense labeling was highest in the presubiculum and parasubiculum in high AD patients. Patient APOE4 genotype also significantly increased the fraction of diffuse amyloid labeling in high AD patients.ConclusionIntegrating machine learning quantification and classification approaches with anatomical segmentation maps allows for rapid characterization of AD pathology across hippocampal subregions and provides new insights into AD progression across the brain.