bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gene‐level Association Study of Alzheimer’s Disease on CHS and FRAM cohorts
Authors: Zhbannikov Ilya , Ukraintseva Svetlana , Wu Deqing , Bagley Olivia , Duan Hongzhe , Holmes Rachel , Yashin Anatoliy I 
Published: December 2023

AbstractBackgroundExperimental, clinical, and genetic epidemiological studies continue clarifying important aspects of Alzheimer’s disease (AD) pathological development. Despite this progress the genetic mechanisms of AD regulation remain unclear. One problem that may contribute to slowing further progress in better understanding of these mechanisms deals with difficulties in replication of the results of genetic association studies. To overcome this problem the idea of using a gene‐based analysis in which all common variation within a candidate gene is considered jointly has been suggested. In this paper we investigate to what extent the use of gene‐based analyses resolves the non‐replication problem.MethodWe performed gene‐level and meta‐analysis association studies for two cohorts: Cardiovascular Heart Study (CHS) and Framingham (FRAM) using Alzheimer’s disease as a primary outcome. To support our findings, we also performed meta‐analysis following gene‐enrichment study for both cohorts. Analysis was performed separately for males and females and for both sexes. Additional covariates were age, smoking. Gene‐level analysis was performed with fastBAT and enrichment analysis with ClusterProfiler on KEGG database.ResultOur analysis identified a list of highly significant genes, such as TOMM40, APOC1 (all with FDR‐adjusted p‐value < 0.05) and pathways, such as Cholesterol metabolism, Fluid shear stress and atherosclerosis, Proteoglycans in cancer, Lipid and atherosclerosis showed statistically significant associations (FDR‐adjusted p‐value < 0.05) in both datasets and sexes.Meta‐analysis revealed TOMM40 (p‐value = 2.35E‐16), APOC1 (p‐value 4.46E‐13), ENSG00000259648 (p‐value = 8.88E‐06), PEX7 (p‐value = 4.40E‐05), ENSG00000287274 (p‐value = 5.38E‐05) genes for men and women combined; men exclusively revealed ENSG00000259648, TOMM40, and APOC1 (p‐value<1.75E‐05) and women TOMM40 and APOC1 (p‐value<6E‐08).Gene‐enrichment meta‐analysis revealed NOD‐like receptor signaling pathway (adjusted p‐value 3.23E‐07), Lipid and atherosclerosis (p‐value = 5.93E‐05), Yersinia (p‐value = 1.17E‐04) for men and women combined; for men only the pathways are Lipid and atherosclerosis (p‐value = 3.61E‐03), TGF‐beta signaling pathway (p‐value = 3.61E‐03), AGE‐RAGE signaling pathway in diabetic complications (p‐value = 5.18E‐03); and for women Cholesterol metabolism (p‐value = 3.49E‐07), Fatty acid degradation (p‐value = 1.04E‐06), PPAR signaling pathway (p‐value = 1.23E‐05).ConclusionIn this work we were able to identify statistically significant genes and pathways showed replication across the studies and are potentially useful for uncovering genetic mechanisms of AD. However, other things such as genetic heterogeneity may influence disease developing and needed further exploration.
A Machine Learning Approach to Identify Individualized Baseline MRI, Plasma, Cardiovascular and Lifestyle Predictors of a 5‐Year Cognitive Decline
Authors: Gebre Robel K , Ramanan Vijay K , Raghavan Sheelakumari , Hofrenning Ekaterina I. , Przybelski Scott A. , Lesnick Timothy G. , Knopman David S. , Machulda Mary M. , Vassilaki Maria , Petersen Ronald C. , Jack Clifford R. , Graff‐Radford Jonathan , Vemuri Prashanthi 
Published: December 2023

AbstractBackgroundThe mapping between risk factors and biomarkers with cognitive performance and decline in the older adults is complex and varies significantly across different age and sex groups. Our goals were to (i) develop a machine learning (ML) model to predict baseline cognition and cognitive decline over a 5‐year follow‐up using only baseline assessments, and (ii) identify important modifiable and non‐modifiable predictors of cognitive decline across different age strata.MethodWe utilized longitudinal data from 2219 participants (mean age (SD): 70 (12) years, 49% male) from Mayo Clinic Study of Aging who had 43 features belonging to cardiovascular health, plasma, MRI (diffusion and structural), and lifestyle measures. Cognition was measured using a global cognition z‐score. We built ML models to predict baseline cognition and rate of cognitive decline over 5‐years (linear regression slopes over all individual followup measurements for each participant) using baseline assessments as inputs. We then built a model for each category separately and as a pooled model with all measures included, incorporating education and sex as demographic features in all models (Fig. 1). We applied a multi‐layer stack ensemble ML technique that utilized repeated k‐fold bagging to maximize accuracy. Importance of the top predictors were assessed in three age strata (55‐65 years (31%), 65‐80 years (46%), 80‐95 years (23%)).ResultThe pooled model explained the largest variance for prediction of baseline cognition and cognitive decline (Fig. 2). In individuals ≤80 years, lower NfL was most predictive of higher baseline cognition. A combination of imaging and plasma features predicted the 5‐year cognitive decline. Additionally, in individuals ≤65 years, diabetes and hypertension were important predictors of faster cognitive decline and in those >80, lower BMI and midlife physical/cognitive activity was an important predictor of faster cognitive decline (Fig. 3).ConclusionThe plasma model was best for baseline cognition prediction and imaging model was best for cognitive decline prediction. There was significant heterogeneity in important features across the age strata. In individuals >65 years, plasma GFAP was the most important predictor of cognitive decline. Our model allowed identification of key risk factors that predicted cognitive decline.
24‐hour Activity Rhythms, Sleep, and Amyloid Pathology in the population‐based Rotterdam Study
Authors: Ho Phuong Thuy Nguyen , Hoepel Sanne J.W. , Luik Annemarie I. , Vernooij Meike W , Neitzel Julia 
Published: December 2023

AbstractBackgroundPrevious research found associations between activity rhythms, sleep, and amyloid pathology (Winer et.al, JAMA Neurology, 2021; Leng et.al, Lancet Neurology, 2019). Shorter self‐reported sleep duration and more napping were robust predictors in the amyloid prediction model we recently developed (A4 Study, n = 4,119) and validated (Rotterdam Study, n = 500). Studies with more population‐representative samples, longer follow‐up time and objective activity/sleep measures are needed to understand whether disturbances of the activity rhythm and sleep increase the risk of developing amyloid pathology.MethodNondemented participants (n = 319) from the prospective, population‐based Rotterdam Study (mean age 69.2±5.3, 47.0% women) were asked to wear an actigraph watch for 7 days before undergoing amyloid 18F‐florbetaben PET on average 7.8 years [range 4‐16] later (Fig.1). 24‐hour activity rhythms (intradaily stability and variability, L5 start time) and sleep (sleep efficiency, sleep onset latency, total sleep time, and wake after sleep onset) were associated with continuous PET readouts (log‐transformed SUVr values) using linear regression models, adjusted for age, sex, education, APOE4 risk allele count, time between actigraphy and PET assessment, difference in type of actigraphy watches, employment status, depressive symptoms, and vascular risk factors (BMI, diabetes, hypertension, physical activity, smoking).ResultHigher intradaily variability, i.e. a more fragmented activity rhythm, was associated with higher amyloid‐PET SUVr (b(SE) = 0.149(0.055), pFDR = 0.049). This association was significantly stronger in APOE4 carriers than non‐carriers (interaction: pFDR = 0.014, Fig.2). Our results remained after excluding participants who probably were already on the AD pathway (excluded lowest 10% of hippocampal volume and highest 10% atrophy on two consecutive MRIs) at the time of measuring activity rhythms. This may suggest that a fragmented activity rhythm is a risk factor rather than a result of amyloid pathology. There were no significant associations of other 24‐hour activity rhythm and sleep variables with amyloid.ConclusionHigher fragmentation of the 24‐hour activity rhythm was associated with more cerebral amyloid pathology eight years later, even after accounting for potential reverse causality. Since activity disturbances are potentially modifiable, early risk factors of AD, further research should be conducted to examine the preventative effect on the development of AD neuropathology.
Targeted metabolomics reveals progression of early Alzheimer’s disease in TgF344 transgenic rats
Authors: Yin Chunyuan 
Published: December 2023

AbstractBackgroundAlzheimer’s disease (AD) is an aging‐related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Current AD diagnosis methods can only identify patients in the advanced stage of the disease, that means when the dementia is already present. This highlights the need for new approaches for the diagnosis of AD at earlier stages, especially before the symptoms appear. In addition, AD is reported to be closely linked with abnormal lipid metabolism. The objective of this study is to identify blood‐based lipid biomarkers to gain a more comprehensive understanding of what causes AD and its subsequent development.MethodA longitudinal study with 4 different ages (12 weeks, 25 weeks, 50 weeks, and 85 weeks) was conducted in TgF344 transgenic (AD) and wild‐type (WT) rats. Metabolomics profiling of EDTA plasma yielded 811 metabolites after quality control. Metabolites profiled included medium and apolar lipids covering 27 lipid classes, signaling lipids including oxylipins, isoprostanes, eicosanoids, bile acids, endocannabinoids. All data was log2 transformed. All statistical analysis and visualizations were performed in R.ResultThe sphingomyelins (SM) were decreased at 25 weeks with AD having more SM expressed than the WT group, while this was reversed at 50 weeks. In 12 weeks, the volcano plot of AD/WT showed more significant lipids on female (p<0.05:15) than male (p<0.05:2). Whereas, in 25 weeks, male (p<0.05:56) showed more significant lipids than female (p<0.05:7). The ratios of an eicosanoid to its seven products were first decreased, then increased over time in both WT and AD group. However, the WT group reached its minimum at 50 weeks, while the AD group reached its minimum at 25 weeks. Sex differences were found in AD/WT among all ages.ConclusionLipidomics data on longitudinal course of potential lipid biomarkers in plasma of WT and AD rats show some effects of AD and/or aging. It is anticipated that this information will contribute to developing lipid biomarker fingerprints for diagnosis and monitoring AD progression distinct from normal aging.
mTOR signaling regulates microglia metabolism and survival via 4E‐BP‐controlled mRNA translation
Authors: Bermudez Sara L , Choi Junghyun , Kim Sunghoon , Mahmood Niaz , Healy Luke M , Sonenberg Nahum 
Published: December 2023

AbstractBackgroundRare loss‐of‐function variants in TREM2 (Triggering Receptor Expressed in Myeloid cells 2) are associated with an increased risk of AD. There are ongoing efforts to therapeutically boost microglial neuroprotective functions by targeting TREM2. However, the intracellular mechanisms underlying these functions in AD are not fully understood. TREM2 signaling maintains metabolic fitness through activation of the mechanistic target of rapamycin (mTOR) allowing microglia to sustain their metabolism during amyloidosis (Ulland et al., 2017). Detrimental decreases in microglial mTOR signalling during amyloidosis independent of TREM2 loss‐of‐function has also been reported (Baik et al., 2019).The mTOR pathway acts upstream of the eukaryotic mRNA translation by suppressing the translation repressor, eukaryotic translation initiation factor 4E (eIF4E)‐binding proteins (4E‐BPs). mTOR inhibits the 4E‐BPs via phosphorylation, thereby releasing eIF4E and allowing mRNA translation to initiate (Hay and Sonenberg, 2004). Work from our group demonstrated that mTOR boosts mitochondrial metabolism through 4E‐BP‐controlled mRNA translation (Morita et al., 2013). Therefore, we aimed to understand the dysregulated mechanisms at the intersection of mTOR‐controlled mRNA translation and metabolism in microglia response to Amyloid‐β (Aß).MethodWe used immunoblotting to investigate the effect on 4E‐BP phosphorylation following chronic exposure to Aß aggregates and TREM2 signaling inhibition in a microglia cell line. We manipulated the mTOR pathway by knocking out the downstream effectors, 4E‐BPs, to promote the translation of 4E‐BP‐controlled mRNAs in microglia in vitro and in vivo. We crossed the microglia‐specific 4E‐BPs knock out mouse with a RiboTag mouse to pull‐down ribosome‐bound mRNAs, providing a genome‐wide pool of actively translating mRNAs (the translatome) in the absence of 4E‐BPs in microglia.ResultWe showed that 4E‐BP‐controlled mRNA translation is inhibited upon chronic exposure to Aß or TREM2 signaling inhibition. The deletion of 4E‐BPs in microglia in‐vitro leads to a decrease in expression of pro‐inflammatory mediators and microglia cell death, and an increase in reliance on oxidative phosphorylation upon exposure to Aß. We also observed that 4E‐BP deletion changes the microglia translatome and decreases expression of inflammatory mediators in vivo after an intracranial Aß infusion.ConclusionWe conclude that mTOR signaling exerts key effects on microglia function and metabolism through 4E‐BP‐controlled mRNA translation.
Head‐to‐head comparison of CSF AD biomarker assays: fully‐automated high‐throughput platforms (ECLIA and CLEIA) vs ELISA
Authors: Günzel Désirée , Güngör Cansu , Goldhardt Oliver , Sommer Patrick , Förstl Hans , Hedderich Dennis , Yakushev Igor , Bruegger Dirk , Grimmer Timo 
Published: December 2023

AbstractBackgroundBiomarkers in cerebrospinal fluid (CSF) can help to diagnose Alzheimer’s disease (AD) and classify patients according to the amyloid/tau/neurodegeneration (ATN) research framework.MethodWe directly compared the performance of three different testing systems, i..e. traditional solid‐phase sandwich ELISA assays (Innotest®, Fujirebio Europe N.V., Gent, Belgium, and IBL, Minneapolis, USA), a fully automated electrochemiluminescence immunoassays (ECLIA) platform (Elecsys® CSF assays measured on the cobas e 411 analyzer, Roche Diagnostics International Ltd, Rotkreuz, Switzerland), and a fully automated chemiluminescent enzyme immunoassays (CLEIA) platform (Lumipulse® G600II, Fujirebio Europe N.V., Gent, Belgium), in 50 early AD patients (64±8.1y) and 25 cognitively unimpaired (CU) controls (65±9.4y)] from the same cohort. Individual CSF biomarkers Aβ42, Aβ40, pTau181 and tTau were measured using all systems. Ratios for pTau181/Aβ42, tTau/Aβ42, Aβ42/Aβ40, pTau181/Aβ42/Aβ40, tTau/Aβ42/Aβ40, pTau181/Aβ40, and tTau/Aβ40 were calculated.ResultAcross all systems, biomarkers and their ratios accurately distinguished AD patients from CU in a comparable manner: ROC: AUCs >0.96 (upper bound of CI:1.0)], except for ELISA Aβ42/Aβ40 [AUC = 0.948 (CI: 0.901‐0.995)]; according to Youden Index (YI)‐derived optimal cut‐offs, sensitivity and specificity were comparably high. YI cut‐offs and manufacturer (MA) cut‐offs were highly consistent. However, for the ECLIA platform (Elecsys®), the YI cut‐off led to significantly higher sensitivity for tTau (97.8% [0.88‐1.00%]/ 80.9%) and pTau181 (97.8% [0.88‐1.00%]/ 83.0%), and higher specificity for Aβ42 (96.0% [79‐100%]/ 76.0%), than MA cut‐off, maybe due to high prevalence of AD. The two fully automated platforms showed markedly reduced maximal coefficients of variation (CV) than ELISA.ConclusionIn addition to increased time efficiency, fully automated platforms showed more consistent results than ELISA assays which favor single analyte measurements. Improved accuracy using YI cut‐offs suggests that in‐house cut‐offs might be important for optimal performance. The two fully automated platforms had comparable accuracy. Thus, the platform of choice might depend on the user’s needs; for instance, the pTau181/Aβ42 ratio (Elecsys®; (AUC: 1.0 [1.0‐1.0], sensitivity 100%, specificity 100%)) combines the two AD hallmarks (A and T) in one test. For ATN classification, the Aβ42/Aβ40 ratio (Lumipulse®, AUC: 1.0 [1.0‐1.0], sensitivity 100%, specificity 100%) might be a more accurate marker for amyloid positivity (A+) than Aβ42 alone.
Recruitment strategies and tactics for INTERCEPT‐AD: A phase I trial of Aβ oligomer‐targeting ACU193 in early Alzheimer’s disease
Authors: Moxon Robyn , Feaster Todd , Carroll Alyssa , Gan Siew Tin , Ziemba Shane , Price Kimber , Skljarevski Vladimir , Sundell Karen L , Sethuraman Gopalan , Hitchcock Janice , Siemers Eric R 
Published: December 2023

AbstractBackgroundClinical trials are essential for testing safety and efficacy of potential treatments for Alzheimer’s disease (AD), but recruitment is often difficult due to a variety of factors. Recruitment tactics in AD clinical trials may include media advertising, physician referrals, patient registries, and referral campaigns. Newer strategies such as social media and community outreach have also been successful in increasing recruitment. However, not all recruitment tactics are successful for every study site and a multi‐faceted approach tailored to each site may be more successful.MethodINTERCEPT‐AD is a phase 1 randomized, double‐blind study of ACU193 in mild cognitive impairment or mild dementia due to AD. Seventeen study sites in the U.S. screened potential participants identified through multiple recruitment tactics which were grouped post hoc into seven categories: site database, external referral, physician referral, site campaign, and three sponsor‐initiated campaigns. Recruitment efforts were tailored to each site’s patient population, community, and staff availability.ResultINTERCEPT‐AD screened 260 participants and 70 were eligible for study participation. Recruitment was completed in January 2023. The sponsor worked directly with individual sites to develop a tailored recruitment plan that considered each site’s target population, historically successful recruitment tactics, and current ability to screen patients from outside sources. Comparisons of the seven types of recruitment sources showed site databases were used most frequently (12/17 sites, 71%) and accounted for the most screenings and eligible participants (107 [41%] and 32 [46%], respectively). Additional data from all recruitment sources and success rates will be presented.ConclusionStudy sites found success using a combination of tactics specifically tailored to their enrollment goals. Sites with robust internal patient databases had higher rates of screening and enrollment. A combination of site‐led and sponsor‐driven recruitment initiatives likely maximized enrollment rates. These findings suggest a strategy using diverse recruitment tactics customized to a study site’s capabilities and patient population may be more successful than a one‐size‐fits‐all approach.
Age‐related and AD pathology‐related differences in neural activation and neural specificity in memory
Authors: Chen Xi , Varghese Leah , Baker Suzanne L. , Jagust William J. 
Published: December 2023

AbstractBackgroundBeta‐amyloid (Aß) and tau deposition is spatially selective in the early stages of Alzheimer’s Disease (AD), with Aß depositing predominately in posterior‐medial (PM) regions and tau accumulating in the medial temporal lobe (MTL) with increasing age before spreading to anterior‐temporal (AT) regions. Functionally, AT and PM are linked to object and scene processing, respectively, and the hippocampus in the MTL is important for integration and forming associations between objects and scenes. Given the differential vulnerability of AT, PM, and MTL to Aß and tau, we examined the behavioral and neural differences in memory for objects, scenes, and object‐in‐scene pairs between young and cognitively normal older adults with and without AD pathologies.MethodTwenty‐three young (19‐34 yrs) and 32 older (60‐91 yrs) participants completed an incidental encoding task on object, scene, and object‐in‐scene pair images during fMRI and a recognition test outside the scanner (Fig.1). Thirty older participants underwent PiB and FTP PET that measured Aß and tau. We conducted univariate whole‐brain analysis and multi‐voxel pattern analysis (MVPA) of AT, PM, and the hippocampus. The MVPA trains a support vector machine to classify the stimulus category (object, scene, pair) based on the ROI’s activation pattern. A higher classification accuracy represents informative neural representation that reflects functional specificity of the region.ResultBehaviorally, older people had worse object (ps<.027) and pair (ps<.017) memory. Greater temporal meta‐ROI tau was related to worse object (p = .032) and pair (p = .022) memory. Whole‐brain analyses showed activations in AT and PM networks for object and scene processing, and additional frontal, temporal, and occipital regions for pair processing (Fig.2A‐C) where older adults, compared to younger adults, showed activation reductions in fusiform, parahippocampal, precuneus, and occipital regions (Fig.2D). The MVPA revealed age‐related reductions in classification accuracy for pairs (Fig.3A) and that greater Aß and tau were related to lower accuracy in the hippocampus (Fig.3B).ConclusionWe found evidence supporting specific vulnerabilities in object and associative memory in older adults correlated with tau pathology. FMRI findings suggest both lower neural activation and lower neural specificity in older adults when processing complex associations. AD pathology appears to contribute to lower neural specificity in the hippocampus.