bluuebunny/crossref_metadata_2025_split huggingface dataset:
Single‐nucleus RNAseq‐derived pseudo‐temporal modeling of neurodegeneration in astrocytes of older brains
Authors: Heath Laura M , Wiley Jesse C , Cary Gregory A. , Poehlman William L. , Gockley Jake , Carter Gregory W , Greenwood Anna K , Mangravite Lara M 
Published: December 2022

AbstractBackgroundWe previously developed a temporal model for unobserved molecular changes occurring during late‐onset Alzheimer’s Disease (AD) for bulk RNA‐Seq and proteomic data. Here we apply this method to single‐nucleus RNAseq data from an AD case‐control cohort and identify Alzheimer’s‐related pathway changes at specific stages of trajectories derived from astrocytes to better understand celltype‐specific gene expression across a continuum.MethodManifold learning defines an order across samples based on their similarity of expression. This ordering estimates pseudotime, an inference of molecular disease progression, quantitatively measured as the distance of each sample from the start of the inferred trajectory. We applied this approach to available snRNAseq data from human postmortem brain samples from the ROS/MAP study (N = 48) (Mathys et al., 2019). We identified a cluster of cells expressing astrocyte‐specific markers. Sex‐specific trajectories were calculated after applying a mutual nearest‐neighbors function to correct the expression matrix values for within‐donor variability. We examined associations between pseudotime and AD case/control status. For each tree branch relative to the root (i.e. the branch with the highest proportion of control cells), we performed differential expression analysis, using a method to account for the hierarchical nature of multi‐subject snRNAseq data (NEBULA, He et al., 2021). We then used gene set enrichment analysis to identify state‐specific significant GO terms from a set that have been curated into 16 distinct AD‐relevant biological domains.ResultPseudotime estimates were significantly associated with LOAD status (females, p = 0.0110; males, p = 0.0011), such that “early” (low pseudotime) samples are enriched for controls, and “late” (high pseudotime) samples are enriched for cases. Genes involved in synapse function and structural stabilization were upregulated, while proteostasis and immune response were downregulated, consistently across pseudotime in males, and later in females. Genes involved in the RNA spliceosome were upregulated at earlier stages but not the latest stage in pseudotime in both sexes. We also observed differences between branches in biological domains encompassing mitochondrial metabolism, vasculature, and apoptosis.ConclusionThis approach to identifying AD‐related gene expression changes across a continuum provides an opportunity to glean new insights about celltype‐specific genetic drivers of AD in the brain.
Longitudinal tau burden partially mediates synergistic influence of vascular risk and amyloid‐beta on cognitive decline in clinically normal older adults
Authors: Yau Wai‐Ying Wendy , Shirzadi Zahra , Rabin Jennifer S. , Properzi Michael J , Schultz Aaron P. , Rentz Dorene M. , Johnson Keith A. , Sperling Reisa A. , Chhatwal Jasmeer P. 
Published: December 2022

AbstractBackgroundVascular risk is increasingly recognized as an important contributor to late‐life cognitive impairment. Elevated vascular risk and amyloid‐beta (Aβ) burden have been synergistically associated with greater cognitive decline, however the underlying mechanisms remain unclear. We examined the combined effects of baseline vascular risk and Aβ on longitudinal tau burden. We further investigated the extent to which tau accumulation mediates the synergistic effects of increased vascular risk and Aβ on prospective cognitive decline.MethodWe included data from 175 older adults (cognitively unimpaired at baseline) from the Harvard Aging Brain Study (HABS) (Table 1). Baseline Aβ burden was measured with Pittsburgh Compound‐B (PiB) PET. Baseline vascular risk was quantified using the office‐based Framingham Heart Study general cardiovascular disease risk score (FHS‐CVD). Tau burden was measured longitudinally with Flortaucipir‐PET in inferior temporal cortex (ITC), an early site of neocortical tau accumulation and a region of interest based on prior work. Cognition was assessed longitudinally using Preclinical Alzheimer Cognitive Composite (PACC). Using linear mixed effects models, we examined the interactive effects of FHS‐CVD and Aβ on longitudinal ITC tau accumulation, adjusting for age, sex and APOE ε4 status. Next, we examined the FHS‐CVD*Aβ interaction using floodlight/Johnson‐Neyman analysis to determine the level of Aβ at which effects on ITC tau accumulation became significant. Using moderated mediation analysis, we further examined whether tau accumulation mediated the FHS‐CVD*Aβ interactive effects on PACC decline.ResultIndividual ITC‐tau trajectories are shown in Figure 1. We found an interaction between higher baseline FHS‐CVD and elevated Aβ with greater longitudinal ITC‐tau accumulation (Figure 2; Table 2). Floodlight analysis showed the FHS‐CVD*Aβ interaction became significant (p<0.05) at PiB‐DVR>1.22, lower than the conventional threshold for amyloid‐positivity (PiB‐DVR>1.32). Moderated mediation models revealed ITC‐tau accumulation partially mediated (30%) the effect of FHS‐CVD on PACC decline at high (PiB‐DVR=1.85: β=‐0.48, p<0.001) but not low (PiB‐DVR=1.17: β=‐0.16, p=0.09) levels of baseline Aβ (Figure 3).ConclusionVascular risk may interact with Aβ to promote cognitive decline partially through accelerating tau accumulation, even at subthreshold levels of Aβ. Further studies are needed to elucidate other mechanisms that mediate the remaining effects of this clinically important interaction.
Translational potential of JAX hAPOE AD mouse model: Phenotypic characterization of cognitive function and amyloid beta
Authors: Altemus Jesse 
Published: December 2022

AbstractBackgroundAPOEe4 is the greatest genetic risk factor for late onset Alzheimer’s Disease (LOAD). The product of this gene, apolipoprotein E isoform ε4, has been characterized as a lipid transporter and many of its receptors and mechanisms have been identified. Studies have shown that APOEe4 exacerbates accumulation of Aβ peptides and correlates with a decline in cognitive function in humans. We investigated the translational feasibility of the humanized APOE3 (JAX #029018) and APOE4 (JAX #027894) mouse model on two hallmark pathologies of LOAD: cognitive decline and b‐amyloid (Aβ) generation in brain.MethodNovel object recognition (NOR), open field (OF), and CatWalk analyses were conducted in 18 month old transgenic mice with humanized APOEe3 homozygous (B6(SJL)‐ApoEtm1.1(APOE*3)Adiuj/J), APOEe3/4, or APOEe4 homozygous genes (B6(SJL)‐ApoEtm1.1(APOE*4)Adiuj/J). NOR analyses included discrimination index, OF center zone time, whereas CatWalk included mobility measures including cadence and speed. Aβ38, Aβ40, and Aβ42 analyses were assessed in brain and plasma.ResultNo differences were detected in NOR discrimination index, NOR combined object exploration time, or OF center zone time across sex or genotype. OF mean speed was lower in APOE3/4 mice compared to APOE4 mice. Aβ38 was not detected in any group. Hippocampal Aβ42 was significantly lower in APOE3/4 mice compared to APOE4 and APOE3 mice. No differences were detected in hippocampal Aβ40 or Aβ42/40 ratios. Plasma Aβ40, Aβ42, and Aβ42/40 were lower in females relative to males. Analyses based on sex and genotype indicated that plasma Aβ40 and Aβ42 were lower in APOE3 females vs APOE3 males, and Aβ42/40 was lower in APOE3 females vs every other group.ConclusionAPOE3/4 heterozygosity may confer a protective effect against Aβ42 deposition in the hippocampus, and female APOE mice consistently exhibit lower plasma Aβ. Translational applicability to humans on measures of cognitive decline and hippocampal Aβ42 deposition at 18 months (human age equivalent ∼56 years) for APOE mice is limited.Acknowledgements: Research supported by NIA R01AG57931 and RF1AG059093
Traumatic Brain Injury Exacerbates Retinal Alzheimer’s Disease Histopathology in the TgF344 Rat Model
Authors: Vergara M. Natalia , Secora Conner N , Vielle Anne , Wang Athena Ching‐Jung , Lenhart Patricia , Salcedo Ernesto , Johnson Noah R , Ahmed Md. Mahiuddin , Chial Heidi J , Boyd Timothy D. , Potter Huntington 
Published: December 2022

AbstractBackgroundAlzheimer’s disease (AD) is a neurodegenerative condition of the central nervous system that affects both the brain and the retina, and can lead to visual impairment. However, the factors that modulate AD pathophysiology leading to the wide range of clinical presentations are not well understood. Notably, traumatic brain injury (TBI), which can arise from sport concussions, military combat and other causes, is associated with a 2.3‐fold higher risk of developing AD and AD‐related dementias. Yet the pathophysiological link between TBI and AD remains poorly understood. In this study we set out to evaluate the effects of TBI, AD, and their combination, on retinal histopathology, by taking advantage of a transgenic rat model (Tg‐F344‐AD) shown to recapitulate the main features of human AD pathology, and combining it with a controlled cortical impact paradigm of repetitive mild TBI (rmTBI).MethodsMale Tg‐F344‐AD rats and F344 wild‐type controls were aged to 12 months and randomly selected to undergo a two‐time unilateral controlled cortical impact (2xCCI; one week apart) that mimics rmTBI, or a sham procedure. The animals were sacrificed at four months post‐CCI to evaluate long‐term effects of the procedure, and the eyes were collected and analyzed using immunofluorescence and standard histological staining techniques to evaluate Alzheimer’s histopathology.ResultsHistopathological analyses at four months post‐impact confirm the presence of AD markers in Tg‐F344‐AD retinas. Moreover, we found that AD increases Aβ deposition and neuroinflammation in the retina, and that TBI enhances the severity of this phenotype by increasing the formation of dense core amyloid plaques.ConclusionsWe have developed and characterized a model to study the pathophysiology of AD in relation to rmTBI in the retina, which could lead to better treatments and to the development of retinal biomarkers for improved AD diagnosis.
Statin responder analysis for precision prevention of Alzheimer’s Disease
Authors: Torrandell‐Haro Georgina , Branigan Gregory L , Rodgers Kathleen E. , Brinton Roberta Diaz 
Published: December 2022

AbstractBackgroundPreviously we identified reduced risk of Alzheimer’s Disease (AD) in statin users (Torrandell‐Haro et al. 2020). Identifying the subgroup of responders is an important step towards a precision statin therapy approach. This study aimed to investigate the effect of statin therapy on the incidence of Alzheimer’s Disease (AD) and other age‐related neurodegenerative diseases (NDD) by therapy, sex, and age group. Moreover, it sought to identify and describe responder vs non‐responder phenotypes to statin therapy based on demographic characteristics, comorbidity burden, and drug exposure.MethodA retrospective analysis was conducted using a US‐based insurance claims dataset of 53 million participants. Inclusion criteria included participants aged 45 years old or older, with no prior history of NDD before statin use, and with claims enrolled for at least 6 months prior and 3 years after start of statin therapy. A propensity score‐matched based on age, gender, region, comorbidities and cci was applied for group assignment. Records were surveyed for a diagnosis of AD 1 year after statin exposure. Sensitivity analyses for the detection of responders based on comorbidities and drug combinations were conducted.ResultOf the 1,293,952 participants who met inclusion criteria, 646,976 participants were exposed to statin therapy and were propensity score‐matched to 646,976 patients without exposure. Statin use was associated with a decreased risk of AD (RR [95% CI]: 0.54 [0.52–0.57]; P < .001) and other NDD. Additionally, men exhibited a greater risk reduction than women, which was driven by atorvastatin and simvastatin. Responders had a higher incidence of obesity and asthma whereas non‐responders were predominantly female and had an overall higher incidence of cardiovascular and cerebrovascular comorbidities.ConclusionStatin use was associated with a reduced risk of AD and other NDD, replicating previous results. Non‐responders were predominantly women with a high incidence of cardiovascular comorbidities. Characterization of responders to statin exposure advances a precision prevention approach, in which prescription guidelines consider neurological health and are adapted for at‐risk populations.This work was supported by the National Institute on Aging (grant R01AG057931), and the Women’s Alzheimer’s Movement and the Center for Innovation in Brain Science to Dr Brinton.
Deep Learning Registration with Scan‐temporal Consistency May Improve Detection of Alzheimer’s Disease Progression
Authors: Dong Mengjin , Xie Long , Wang Jiancong , Das Sandhitsu R. , Wolk David A. , Yushkevich Paul A. 
Published: December 2022

AbstractBackgroundStructural changes in Alzheimer’s disease (AD) can be detected as atrophy in the medial temporal lobe (MTL), which is normally studied by applying volumetric or deformation‐based morphometry (DBM) to longitudinal scans. However, conventional DMB methods are computationally intensive and may not estimate the atrophy accurately due to the noise in the longitudinal images, e.g., head motion or MRI artifacts. We hypothesize that deep learning models can be trained to estimate atrophy from longitudinal MRI scans and are more sensitive to biological changes rather than artifactual.MethodTwo models were trained on longitudinal T1 MRI from 155 ADNI participants and tested on 326 participants. First, a deep learning‐based registration model Voxelmorph (Balakrishnan et al., 2019) was applied to generate a deformation field to calculate MTL atrophy rate for each pair of images. Hypothesizing that noise is independent of time while neurodegeneration is time dependent, we then extended Voxelmorph to incorporate a loss that encourages the model to correctly infer the input scan temporal order (STO). In addition, in order to preserve the deformable registration nature of Voxelmorph, it’s weights were held fixed during network training, and instead a U‐Net reconstruction network was introduced, to synthesize inputs to Voxelmorph. Models were compared with a DBM method ALOHA (Das et al., 2012).ResultVoxelmorph generated atrophy measurements comparable to ALOHA in terms of differentiating AD stages (Figure 2) and STO consistency (Table 2). Voxelmorph + STO model has increased STO consistency and can more significantly differentiate disease stages (in absolute terms), especially between the normal controls and preclinical AD.ConclusionDeep learning‐based registration method Voxelmorph achieves comparable performance in longitudinal analysis of subtle changes as in DBM method ALOHA. With the introduction of a scan temporal order loss and reconstruction network, the model achieves higher sensitivity to disease stage, potentially because it learns to discount longitudinal changes that are related to noise and artifact. This may lead to more sensitive biomarkers for disease monitoring in clinical trials of early AD.
The Potential Alzheimer’s Disease Drug, GM‐CSF, Enhances the Anti‐Viral Immune Response and Reduces Mortality in a Mouse Model of COVID‐19
Authors: Kendall Lon V. , Boyd Timothy D. , Sillau Stefan H , Bosco‐Lauth Angela , Markham Neil , Fong Derek , Clarke Penny , Tyler Kenneth L. , Potter Huntington 
Published: December 2022

AbstractBackgroundCOVID‐19 results in increased expression of inflammatory cytokines and Alzheimer’s disease (AD) biomarkers of neuronal damage, but inflammation‐targeting clinical trials have yielded poor to mixed results. Our studies of other disorders with an inflammatory component, including AD, chemobrain, Down syndrome, normal aging, and West Nile Virus infection, showed that treatment with the ‘pro‐inflammatory’ cytokine granulocyte‐macrophage colony‐stimulating factor (GM‐CSF) in humans or mouse models alleviated clinical, behavioral, and pathological features. Most recently, we completed a Phase 2 clinical trial (NCT01409915) using sargramostim/Leukine (rhuGM‐CSF) in mild‐to‐moderate AD participants, which showed that three weeks of sargramostim treatment improved MMSE scores and improved ATN blood biomarkers of AD pathology (Potter et al., 2021). Therefore, we proposed that human GM‐CSF may be repurposed to promote both the innate and adaptive immune responses in COVID‐19 patients to reduce viral load and mortality.MethodWe investigated GM‐CSF’s effects in eight‐week old male and female human ACE2 (hACE2) transgenic mice infected intranasally with 104 PFU of SARS‐CoV‐2 virus and followed them for 14 days. Mice received daily IP injections of either recombinant murine GM‐CSF or saline. Viral titers, anti‐spike protein antibody levels, and mortality were assessed.ResultsIntranasal inoculation of hACE2 transgenic mice with 104 PFU SARS‐CoV2 virus resulted in high viral titers in lungs and brains and over 85% mortality. GM‐CSF treatment beginning one day post‐infection increased anti‐spike protein antibody titers, lowered mean lung viral titers proportionately (p = 0.0020), and increased the odds of long‐term survival by up to 5.8‐fold (p = 0.0358), compared to saline.Conclusion
GM‐CSF represents a new approach to the treatment of COVID‐19 by recruiting inflammation and the immune system to attack SARS‐CoV‐2 infection and promote survival.
GM‐CSF is likely to have a significant advantage over current approaches to the treatment of COVID‐19, including anti‐virus monoclonal antibodies, drugs designed to inhibit viral replication, and immunosuppressants, because, unlike short‐term antivirals, it activates the endogenous immune system, with likely long‐term increases in immune memory required for protection against re‐infection,
Based on its mode of action as a natural stimulator of the immune response, GM‐CSF should be effective against all current and future SARS‐CoV‐2 variants.
Racial Differences in BMI and Cognitive Performance in Persons Aged 80 and Older
Authors: Trammell Antoine R , Goldstein Felicia C , Parker Monica W , Hajjar Ihab 
Published: December 2022

AbstractBackgroundThere is evidence that late‐life BMI is protective against cognitive decline. As Alzheimer’s disease (AD) study samples do not reflect the general population, it is not clear if there are racial differences this finding. We investigated late‐life BMI and cognitive performance in a biracial sample.MethodUsing ANOVA or chi‐square, demographic, physical, and cognitive test scores were compared in persons age 80 and older by race. Cognitive groups were based on global CDR. Normal cognition (NC) was defined per global CDR = 0 and impaired cognition per global CDR = 0.5,1,2, or 3. Mixed models adjusted for age, sex, and education with BMI as the predictor evaluated cognitive test scores by race for study visits from 2005 to 2020. The significance level for all tests was p < 0.05.ResultData for 9,581 persons was analyzed. The sample was mostly white (86.6% vs. 13.41%, <.0001). AA were younger (86.4 vs. 86.9, <.0001), had higher BMI (26.8 vs. 25.5, <.0001), fewer years of education (13.4 years vs. 15.3 years, <.0001), and lower MOCA scores (18 vs 21, <.0001) than whites. In AA, mixed models showed associations between higher BMI and executive function (Trial Making Test‐B:β = ‐1.51,p<.01) and episodic memory (Craft delayed: β = .07,p = .01). In whites, there was an association between higher BMI and picture naming (Multilingual Naming: β = .09,p = <.01), attention (Trail Making Test‐A:β = ‐.12, p = 0.3), and working memory (Number span, backward: β = .03,p = .01).ConclusionLate‐life BMI is associated with improved cognitive domain performance that vary by race. Further study is needed to prospectively replicate the findings in multiethnic cohort and explore factors underlying the differences.
Optimizing Placebo Effects in Depressed Older Adults: Enhancing Processing Speed and Executive Functioning with Computerized Cognitive Training
Authors: Premnath Pranitha Y , Egglefield Dakota , Schiff Sophie , Bursky Mikell , Zilcha‐Mano Sigal , Rutherford Bret R , Sneed Joel R 
Published: December 2022

AbstractBackgroundMajor Depressive Disorder (MDD) is a leading cause of disability and a severe public health concern for older adults. Slowed processing speed (PS) and executive dysfunction (ED) often complicate antidepressant treatment response in this population. Treatment non‐response in older adults with PS deficits may be caused by failure to form expectations about treatment, suggesting that targeting PS deficits prior to antidepressant treatment may restore the capacity to form expectations and improve treatment response. One way of improving PS may be through the use of computerized cognitive training (CCT), through structured drills and repeated practice. This study aims to 1) determine whether PS mediates the relationship between CCT and expectancy, and 2) assess the effects of CCT and expectancy manipulations on depressive symptoms in geriatric populations.Method100 older adults (≥ 60 years; Hamilton Rating Scale for Depression (HRSD) score ≥ 20) with PS deficits will be randomized to either CCT or control (Solitaire) for 4 weeks. Both conditions will train for 25 minutes/day, 7 days/week. At the conclusion of this four‐week period, patients will be randomized to high versus low expectancy treatment conditions. Patients assigned to the low expectancy condition will be told they will receive either placebo or escitalopram. Patients assigned to the high expectancy condition will be told they will receive escitalopram for 8 weeks. All participants will receive escitalopram for 8 weeks. Neuropsychological assessments will be completed at baseline, week 4, and week 12; MRI acquisition at baseline and week 4. Clinical assessments will be conducted weekly throughout the study.ResultsRecruitment is in progress and is expected to be completed by April 2024 followed by data analysis.ConclusionIn light of the increased use of virtual healthcare platforms in response to the pandemic, developing accessible, technology driven interventions that address antidepressant non‐response and improve quality of life in older adults is increasingly important. This novel experimental, therapeutic approach tailors existing interventions to optimize outcomes for antidepressant treatment in late‐life depression and addresses significant challenges in current geriatric mental health research.