bluuebunny/crossref_metadata_2025_split huggingface dataset:
Early loss of astrocyte p38alpha MAPK has sexually dimorphic impacts on measures of neuronal excitability and cytokines in aged mice
Authors: Gant John C , Coleman Meggie J. , Davis Verda A , Norris Christopher M. , Van Eldik Linda J. J. , Braun David J 
Published: December 2023

AbstractBackgroundThe p38alpha MAPK (p38) signaling pathway is ubiquitously expressed in the brain where it plays central roles in neuroinflammation, metabolism, synaptic development and communication, and modulation of blood brain barrier integrity. Its activity in astrocytes has largely been described as immunomodulatory; however, it has also recently been reported to contribute to long term depression and associated behaviors. Astrocytes are also known to be important to healthy brain aging and neuroprotection, but the specific involvement of astrocytic p38 signaling is unclear. We therefore used conditional KO mice to determine how the loss of p38 in astrocytes early in adulthood (3 months) affects synaptic function and cytokine expression phenotypes in later life (20+ months).MethodMice were bred homozygous for floxed p38, heterozygous for a Rosa reporter, and with or without tamoxifen‐inducible Cre recombinase under control of the astrocyte aldh1l1 promoter. When animals were about 3 months of age they were placed on tamoxifen diet (400 ppm) for 4 weeks, after which they were returned to standard chow and allowed to age until 20‐24 months prior to undergoing electrophysiological endpoints or collection of brain tissue for biochemical analyses. Electrically evoked field excitatory post synaptic potentials (fEPSPs) were recorded in hippocampal CA1 stratum radiatum in acutely prepared brain slices and cytokines from the whole hippocampus were measured from a subset of mice that did not undergo electrophysiological recordings.ResultMales and females showed baseline differences in measures of I/O, fiber volley amplitude, early vs. late potentiation, and paired pulse facilitation. Additionally, knockout of astrocyte p38 generally had opposite effects on these parameters in males and females. Interestingly, the pattern of hippocampal IL‐1β cytokine levels mirrored these differences.ConclusionThese data indicate that there may be qualitative differences in male versus female brain aging that are differentially impacted by astrocyte p38 signaling. The implications of these findings are currently being explored to determine the underlying mechanisms for these sexually dimorphic effects and further determine what functional differences may exist for hippocampal‐dependent behaviors.
Inter‐network connectivity of early Aβ and tau‐seeded functional networks is associated with longitudinal tau accumulation in at‐risk cognitively normal older adults
Authors: Fonseca Corrina S. , Harrison Theresa M. , Adams Jenna N. , Ziontz Jacob , Jagust William J. 
Published: December 2023

AbstractBackgroundFunctional connectivity (FC) between networks may be related to Aβ and tau interactions in preclinical Alzheimer’s disease (AD). Here, we investigated associations between inter‐network FC of early Aβ and tau‐seeded networks, APOEe4, global Aβ, and longitudinal tau accumulation in cognitively healthy older adults (OA).MethodVoxel‐wise PiB‐PET and FTP‐PET OA images were ordered by increasing pathology burden and iteratively compared to a reference set of young adult (YA) PET images. Peak voxels of the earliest significant differences were matched to Brainnetome ROIs and used to construct separate Aβ and tau‐seeded networks within a group average YA FC matrix (Figure 1). Next, the average inter‐network FC between all node pairs between the networks was calculated in OA (n = 60; aged 77.6±5.5). Longitudinal FTP slopes of the entorhinal cortex (EC) and inferior temporal gyrus (ITG) were estimated using linear mixed effects models. Linear regression was used to assess relationships between baseline inter‐network FC, APOEe4 status, baseline global PiB, and both a prior ROI and voxel‐wise FTP slopes.ResultIn all OA, inter‐network FC was not associated with baseline global PIB (t(58) = ‐0.23, p = 0.82), EC FTP slope (t(58) = 0.49, p = 0.63), or ITG FTP slope (t(58) = ‐1.45, p = 0.15). There was a significant interaction between baseline global PiB status and inter‐network FC where FC was negatively associated with ITG FTP slope in PiB+ participants (t(56) = ‐2.01, p = 0.04; Figure 2A). There was also a significant interaction between APOEe4 status and inter‐network FC in predicting ITG FTP slope with a negative relationship only in e4 carriers (t(56) = ‐3.19, p = 0.003; Figure 2B). Voxel‐wise analyses revealed significant negative relationships between inter‐network FC and FTP slope in the left fusiform gyrus and ITG in PiB+ participants and e4 carriers, respectively (Figure 2C‐D).ConclusionLower baseline inter‐network FC of early Aβ and tau‐seeded networks was associated with greater longitudinal tau accumulation outside of the medial temporal lobe (MTL) in cognitively normal OA with higher AD risk (i.e., elevated Aβ or APOEe4). Our results suggest that this network disruption is associated with faster rates of tau accumulation outside of the MTL, possibly reflecting a later stage in the preclinical progression to AD.
Classification of amyloid‐positivity using memory scores in subjects with early‐onset Alzheimer’s disease
Authors: Bushnell Justin , Hammers Dustin B. , Apostolova Liana G. , Kramer Joel H. , Carrillo Maria C. , Dickerson Brad C. , Rabinovici Gil D. , Clark David G. 
Published: December 2023

AbstractBackgroundThe Rey Auditory Verbal Learning Test (RAVLT) is a common neuropsychological test for characterizing episodic memory deficits. Performance of the RAVLT has been described in numerous presentations of cognitive impairment. However, there is limited research for analyzing the predictive value of RAVLT scores. We examine the ability of the RAVLT to classify individuals with early‐onset cognitive impairment in terms of amyloid positivity.MethodWe transcribed RAVLT recordings from 214 cognitively impaired subjects in the Longitudinal Early‐Onset Alzheimer’s Disease Study (LEADS). Each RAVLT test consists of eight tasks: five learning trials, an interference list, short delay free recall, and long delay free recall. Subjects were stratified by amyloid positivity (EOAD vs. EOnonAD). We calculated traditional and novel scores based on counts (raw score, primacy, recency) and timings (stopping time and speed). To split the data into training and test sets, we selected subjects such that Cohen’s D was below 0.05 between nuisance covariates (age and education) and included similar proportions of each sex. Due to the imbalance of classes in the data set, we generated 1,000 equal‐sized bootstrap samples of cases and controls (n = 200) and fit a random forest classifier for each sample. Subjects in the test set were classified by majority vote. This classification process was applied to three different sets of RAVLT variables for comparing performance.ResultWith raw scores and demographics alone, we achieved AUC of 0.839. We obtained an AUC of 0.841 using variables with possible utility identified in previous work. The best model used only speed scores and demographic variables, with an AUC of 0.858.ConclusionWe marginally improved the classification accuracy of amyloid‐positive subjects over raw scores alone. Further work is needed to determine the utility of novel scores in predicting biomarker positivity.
Prediction of future dementia for MCI patients using a novel multimodal machine learning framework
Authors: Cirincione Andrew Joseph , Lynch Kirsten M , Choupan Jeiran , Varghese Bino , Sheikh‐Bahaei Nasim , Pandey Gaurav 
Published: December 2023

AbstractBackgroundPrediction of future dementia for patients with mild cognitive impairment (MCI) is a significant clinical goal, so that the identified cases can benefit from treatments. Currently, the clinical standard for diagnosing dementia utilizes multimodal data like cognitive tests, MRI and PET scans, and other biomarkers. However, efficiently and objectively analyzing these complex, diverse data can be difficult in the clinical setting, contributing to high rates of underdiagnosis or misdiagnosis of dementia at early stages. Machine learning (ML) offers a potentially more efficient and objective methodology to aid the prediction of future dementia for MCI patients from these multimodal data.Methode recently developed Ensemble Integration (EI), an ML framework to advance predictive modeling from multimodal data by leveraging complementarity among the data modalities (Li et al, Bioinformatics Advances, 2022). In the current work, we conducted the first assessment of EI’s ability to predict the future development of dementia among MCI patients using multimodal data from The Alzheimer’s Disease Prediction of Longitudinal Evolution (TADPOLE) challenge (Marinescu et al, Predictive Intelligence in Medicine, 2019) (Table 1). We developed an EI‐based predictive model of dementia from data of 672 MCI patients collected at their first visit (baseline), and rigorously evaluated this model and benchmark methods on baseline data from two separate test sets (Figure 1).ResultFor predicting the future development of dementia among MCI patients, the EI‐based model performed better on two test sets (AUROC = 0.77/0.78, sensitivity = 0.71/0.75, specificity = 0.74/0.75) than the more commonly used XGBoost (AUROC = 0.66/0.67, sensitivity = 0.59/0.63, specificity = 0.73/0.71) and deep learning (AUROC = 0.63/0.64, sensitivity = 0.78/0.62, specificity = 0.41/0.55) approaches. This model also suggested MRI‐derived measurements of the cortical thickness, surface area and volume of regions in the thalamus and parahippocampus to be predictive of this outcome (Table 2).ConclusionEI is an effective framework for predicting if an MCI patient will develop dementia in the future, as well as identifying neuroanatomical features that may be associated with this progression.
A novel analytic approach to examine the relationship between APOE4 allele status and CSF complement‐related proteins and signaling pathways
Authors: Orenduff Melissa , Sykes David , Cooter Mary , Morshed Nader , Stevens Beth , Lucas Joseph E. , Lutz Michael W , Berger Miles 
Published: December 2023

AbstractBackgroundThe mechanisms by which the APOE4 allele contributes to AD risk are unclear. Here, we tested the hypothesis that in cognitively normal adults, APOE4 allele is associated with a decrease in complement‐related proteins and downregulation of complement‐related pathways.MethodUntargeted LC‐MS/MS proteomics was conducted on CSF samples (n = 118) from APOE3/3, APOE3/4 and APOE4/4 individuals over age 60, with groups matched on age and sex. We developed a novel analytic approach (which we term “up/down analysis”) based on the directional consistency of the β‐coefficients from a linear model for the intensities of peptides mapping to a given protein with respect to APOE4 carrier status (APOE4‐/ APOE4+). The analysis involves two steps: 1) The direction of the beta‐coefficient (positive/up or negative/down) for the level of each peptide (the outcome variable) due to APOE4 carrier status was derived from linear regression models adjusting for age, sex, and study. 2) These β‐coefficients were tallied for all peptides from a given protein (i.e. number of positive/up versus negative/down β‐coefficients). We then tested the probability under a binomial distribution that the distribution of up versus down peptides from each protein would be as, or more extreme than what we found, with a = 0.05. Pathway enrichment analysis (using p‐values from binomial tests) was conducted using GAGE and functional annotations based on KEGG. We used Benjamini‐Hochberg for multiple comparison correction.ResultWe found 158 proteins for which the direction of peptide intensity was consistently up (+β‐coefficient) or down (‐β‐coefficient) with respect to APOE4 status beyond what would be expected by chance. Of these 158 proteins, 37 were from the complement pathway. Pathway enrichment analysis further supports the role of the complement system; as the top differentially expressed pathway (FDR <0.05) was related to complement signaling.ConclusionOur results suggest that APOE4 carriers have lower CSF levels of key complement cascade proteins, which has previously been viewed as an indicator of complement activation. These results may provide insights into the mechanisms by which APOE4 and associated complement‐related proteins and signaling pathways ultimately contribute to increased AD risk.
Protective effects of mitochondrial genome abundance on Alzheimer’s Disease: a Mendelian randomization study
Authors: Alvarez Brian D , Molina Carmen Romero , Wilkins Heather M , Pa Judy , Swerdlow Russell H , Goate Alison , Andrews Shea J 
Published: December 2023

AbstractBackgroundMitochondrial DNA copy number (mtDNAcn) is a surrogate measure of mitochondrial function that is associated with higher cognitive performance and reduced risk of AD neuropathology. Here, we use two‐sample Mendelian randomization (MR) to estimate the causal association of blood‐based mtDNAcn on AD/dementia while controlling for latent confounders, sample overlap, and testing bidirectional effects.MethodGWAS Summary statistics of mtDNAcn were procured from three separate studies using various bioinformatic methods to estimate mtDNAcn; microarray, whole exome sequencing (WES), and whole genome sequencing (WGS) datasets using the UK Biobank (UKBB) and Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. Longchamps et al., 2021. uses quantitative PCR (CHARGE) and WES, WGS (UKBB). Hagg et al., 2021 use microarray approximation, as well as WGS and qPCR. Chong et al., 2022 employ the novel AutoMitoC in addition to WES data (UKBB). Kunkle et al., 2019 employed late‐onset clinical AD cases/controls (n = 94,437), and Bellenguez et al., 2022 (n = 487,511) used AD/dementia from clinical and proxy cases/controls. MR estimates the causal association between mtDNAcn and AD and AD/dementia using IVW analysis, and MR Egger, Weighted Mode, and Weighted Median as sensitivity analysis. Latent Heritable Confounder‐MR (LHC‐MR) was used to investigate bidirectional effects in the presence of sample overlap between the mtDNAcn datasets and AD/Dementia.ResultThere were no significant causal associations of mtDNAcn on AD or AD/dementia using univariate MR analysis. However, using LHC‐MR, higher genetically predicted mtDNAcn was causally associated with AD/dementia using all three mtDNAcn datasets (OR [95%CI]: Chong = 0.84 [0.78, 0.90], p < 0.001; Longchamps = 0.89 [0.82, 0.96], p = 0.003; Hagg = 0.84 [0.78, 0.91], p < 0.001). In the reverse direction, genetic liability for AD/dementia was causally associated with higher mtDNAcn levels (β [SE]: 0.10 [0.05], p = 0.017).ConclusionHigher blood‐based mtDNAcn was causally associated with reduced risk of AD, with limited evidence to suggest a bidirectional effect. Discrepancies between univariate MR results and LHC‐MR may be explained by the increased power and the inclusion of a latent confounder.
Disentangling the causal effects of education and participation bias on Alzheimer’s disease using Mendelian Randomization
Authors: Chatterjee Aadrita 
Published: December 2023

AbstractBackgroundHigher education has been associated with reduced risk of Alzheimer’s Disease (AD) based on previous epidemiological research. However, the relationship between education and AD could be influenced by participation bias whereby people with higher education may be more likely to be enrolled in studies. In this study, we use two‐sample Mendelian randomization (MR) to investigate the potential effects of education and participation bias on AD and further investigate their relationship.MethodGenome‐wide association summary (GWAS) statistics were obtained from GWAS’s of educational attainment (n = 1,100,000), participation in an optional mental health questionnaire (MHQ) of the UK Biobank (n = 451,036) and AD (n = 94,437). Independent genome‐wide significant (r2 = 0.001, 10Mb window, p<5×10−8) single nucleotide polymorphisms (SNPs) were extracted from the exposure summary statistics and harmonized with the outcome SNPs. Inverse variance weighted (IVW) meta‐analysis was the primary MR method and sensitivity analyses included MR Egger, Weighted Median and Weighted mode. Cochran’s Q statistic was used to test for heterogeneity, MR Egger intercept for pleiotropy, and radial MR to identify outliers.ResultGenetically predicted higher educational attainment was causally associated with a reduced risk of AD (IVW OR [95% CI]: 0.691 [0.548, 0.832], p = 3.29e‐07). Similarly, participation in the MHQ was also associated with a reduction in AD risk (IVW OR [95% CI]: 0.987 [0.975, 0.998], p = 0.031]. Our analysis showed no evidence of heterogeneity or pleiotropy and radial MR analysis did not identify any significant outliers, indicating that our univariate MR analyses were robust.ConclusionUnivariant MR analyses indicated that education and participation were both associated with a reduced risk of AD. However, further investigation using multivariable MR is necessary to fully evaluate their independent effects on AD through multiple clumping and proxy variant identification steps. This will allow us to see if the protective effect of education on AD is mediated by participation bias.
Comparison Among Different Modeling Methods on the Mini‐Mental State Examination
Authors: Wu Xian , Fardo David W. , Katsumata Yuriko 
Published: December 2023

AbstractBackgroundThe Mini‐Mental State Examination (MMSE) is commonly used in clinical and research settings. It is recognized that MMSE is not sensitive to detect cognitive impairment 1. One of the potential reasons may come from an inappropriate statistical model. MMSE raw scores do not follow a normal distribution which is an inherent assumption in a linear regression model. Previous studies either ignored the normality assumption or used MMSE transformed scores 2. Tobit modeling explicitly handles ceiling effects by making best use of ceiling data information for estimation 3. In this study, we compared three different modeling methods to investigate associations between risk factors (we chose hypertension here) at baseline and change in MMSE scores over time.Method46,513 participants in the National Alzheimer’s Coordinating Center (NACC) Uniform Data Set (UDS) were included in this study. Hypertension was defined if they met one of the following criteria: (1) systolic blood pressure (BP) ≥ 140 or diastolic BP ≥ 90; (2) self‐reported or clinician‐assessed hypertension; and (3) current use of any type of antihypertensive medication. We compared three modeling methods in all participants and three subgroups: normal cognition (NC); mild cognitive impairment (MCI); and dementia. The three models were: (1) MEM: mixed‐effect modeling on MMSE raw scores; (2) Transformed: MEM on transformed MMSE scores with square root of (30‐MMSE); and (3) Tobit: mixed‐effect Tobit modeling on MMSE raw scores.ResultMMSE score distributions were left‐skewed in all participants and three subgroups, particularly in the NC group (Figure 1). Hypertension at baseline was associated with change in MMSE scores over time in all participants, whereas discrepancy in associations existed across three subgroups. Smallest standard errors, Akaike information criterion (AIC), and Bayesian Information Criteria (BIC) values were generated by Tobit models (see Table 1 for results from regression and model comparison).ConclusionBased on results from the model comparison, Tobit models were superior across all participants and three subgroups. Moreover, results from Tobit regression modeling can be interpreted as the ways that we explain results from multiple linear regression. In the future, we will apply Tobit models to discover associations between different risk factors and cognitive functions.
The “microbiome‐gut‐brain axis” in Alzheimer’s disease and its role in neurocognitive decline
Authors: Loew Ethan B , Sallis Benjamin , Tracy Matthew , Haran John P , McCormick Beth 
Published: December 2023

AbstractBackgroundAlzheimer’s disease (AD) is a progressive neurocognitive disease characterized by amyloid beta plaques and neurofibrillary tangles in the brain along with inflammation both in the brain and systemically. This has led to the theory of microbial communities or infections as being causative in the development of neuroinflammation as well as immunosenescence and inflamm‐aging seen in AD. Our own research has demonstrated a decreased abundance of anti‐inflammatory taxa and an increased abundance of pro‐inflammatory taxa in the gut microbiome of AD patients. However, it is unclear how the AD microbiome exerts effects on the central nervous system.MethodWe have performed gut microbiome profiling, analysis of immune cell populations in serum, blood cytokine profiling, and cognitive assessments of AD older adults at 90‐day intervals.ResultIn our early data collected from this ongoing study we have observed changes in B‐cell populations with an increased abundance of class‐switched B‐cells in older adults with greater levels of cognitive impairment (Spearman R = 0.33, p = 0.001). We have further demonstrated that fecal transfer of the microbiome of older adults with AD into mice promotes B‐cell class‐switching when compared with cognitively impaired older adults without AD (Percent total B‐cells, AD 75.12 (SD 10.41) vs ND 50.02 (SD 11.90) p<0.016). Additionally, we have discovered a loss of phytoestrogen‐metabolizing bacteria such as Adlercreutzia equolifaciens among AD older adults with rapidly progressing dementia. Phytoestrogens have been previously identified in protecting the intestinal epithelium from oxidative stress and epithelial permeability. Our work suggests that that the phytoestrogen (s)‐equol, produced by A. equolifaciens, confers resistance to epithelial damage in the setting of oxidative stress and bacterial lipopolysaccharide.ConclusionWe propose that Adlercreutzia equolifaciens and similar phytoestrogen‐metabolizing bacteria will better maintain epithelial homeostasis in the setting of inflammation while a gain of pro‐inflammatory taxa in AD leads to intestinal barrier disruption. This leads to increased antigen presentation, immune cell dysregulation, and ultimately cognitive decline. This continuing work aims to further establish the connection between AD related neurocognitive decline, the microbiome, and immune system.