bluuebunny/crossref_metadata_2025_split huggingface dataset:
Amyloid predicts longitudinal atrophy in non‐demented individuals: Results from the AMYPAD Prognostic & Natural History study
Authors: Pieperhoff Leonard , Lorenzini Luigi , Mastenbroek Sophie E , Shekari Mahnaz , Wink Alle Meije , Wolz Robin , Grootoonk Sylke , Overduin Bert , Alves Isadora Lopes , Ritchie Craig , Boada Mercè , Marquié Marta , Scheltens Philip , Vandenberghe Rik , Hanseeuw Bernard J , Martinez‐Lage Pablo , Payoux Pierre , Visser Pieter Jelle , Schöll Michael , Frisoni Giovanni B , Stephens Andrew W. , Buckley Chris J , Farrar Gill , Jessen Frank , Grau‐Rivera Oriol , Gispert Juan Domingo , García David Vállez , Collij Lyduine E. , Barkhof Frederik ,   the AMYPAD consortium
Published: December 2023

AbstractBackgroundWhile the association between tau deposition on PET and brain atrophy on MRI is well documented in the literature, the relation between amyloid deposition and neurodegeneration in the earliest stages of Alzheimer’s disease (AD) is less studied. We investigated this relationship in a large cohort of non‐demented individuals.MethodWe included 1355 participants from the AMYPAD Prognostic & Natural History study (PNHS) with available MRI and amyloid‐PET. Among those, 753 had longitudinal MRI, with a mean follow‐up time of 3.47 years (SD = 1.55). The FreeSurfer 7.0.1 longitudinal pipeline was used to measure gray matter (GM) thickness and volumes in 40 regions (Reuter et al., 2012; Desikan et al., 2006). From PET scans, cortical amyloid burden was assessed globally using the Centiloid (CL) method. MRI‐derived regional volumes and thickness were harmonized across sites with neuroCombat (Fortin et al., 2018); all variables were Z‐scored.Linear mixed‐effect models were used to investigate the effect of amyloid burden at baseline and its interaction with time on regional volume and thickness measurements. Models were corrected for age, sex, and CDR. P‐values were FDR‐adjusted.ResultCohort characteristics are shown in Table 1. Decreasing cortical thickness and volumes were observed over time in most regions. At baseline, higher amyloid burden was related to smaller volumes of the hippocampus, amygdala, putamen, and lateral ventricles; and thinner entorhinal cortex, precuneus and posterior cingulate cortex. At follow‐up, subjects with higher baseline amyloid burden showed greater loss of volume and thickness in mainly temporal and parietal regions, as well as amygdala and hippocampal volume (Figure 1).ConclusionIn the largely asymptomatic AMYPAD PNHS cohort, we show that amyloid burden at baseline is predictive of future neurodegeneration, especially affecting temporal volume and parietal thickness. Cortical thickness was slightly more sensitive towards amyloid burden at baseline, while loss of cortical volume was greater than loss of thickness.
Genome‐wide association meta‐analysis identifies a novel LRBA locus for brain age acceleration in two independent Korean Cohorts
Authors: Kim Bo‐Hyun , Nho Kwangsik , Huang Yen‐Ning , Risacher Shannon L , Kim Jun Pyo , Yi Dahyun , Byun Min Soo , Kim Hee Jin , Won Hong‐Hee , Saykin Andrew J. , Lee Dong Young , Seo Sang Won 
Published: December 2023

AbstractBackgroundAging is a key risk factor for Alzheimer’s disease (AD) and has a significant structural impact on the brain. Machine learning approaches have been developed to estimate the biological age of the brain from MRI scans. Here, we performed genome‐wide association (GWA) analysis of accelerated brain aging in two independent Korean AD cohorts.MethodParticipants included 1,738 Korean older adults from two independent cohorts that consisted of preclinical and clinical stages of AD (n = 1,209 from the K‐ROAD cohort (Korea‐Registries to Overcome and Accelerate Dementia Research Project) and n = 529 from the KBASE cohort (Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease)). Brain age was estimated from structural MRI scans using brainageR. Brain age acceleration (predicted brain age ‐ chronological age) was calculated for each participant. Genetic variants associated with brain age acceleration were assessed after accounting for age, sex, APOE e4 carrier status, and diagnosis. Meta‐analysis was performed using METAL, followed by transcriptomic analysis. Polygenic risk scores for brain age acceleration were calculated with PRS‐CS using GWAS meta‐analysis summary statistics, followed by molecular imaging analysis.ResultGWA meta‐analysis identified six intronic SNPs in the LRBA (Lipopolysaccharide responsive Beige‐like Anchor) locus on chromosome 4 as significantly associated with brain age acceleration with the most significant signal at rs7699001 (Fig. 1; p<5´10−8). LRBA is highly expressed in the brain, especially in microglia. The LRBA gene is differentially expressed in AD compared to cognitively normal older adults. rs7699001 was associated with expression levels of the LRBA gene in tissue from several organs including brain. rs7690001 has been reported to be associated with inferior parietal cortical thickness in non‐Hispanic whites (p = 5´10−3). Polygenic risk scores of brain age acceleration in the K‐ROAD cohort were significantly associated with amyloid‐β deposition measured from amyloid PET scans (Fig. 2; p = 2.7´10−4).Conclusion
LRBA is a novel genetic risk factor for brain aging in the Korean population with relevance for AD. The microglial LRBA protein may be involved in phagocytosis, which has been implicated in aging processes and AD progression. Replication in other populations and mechanistic follow‐up studies are warranted.This work was supported by U01AG072177.
Micro‐RNA dysregulation in brains of Alzheimer’s disease mice models: A systematic review
Authors: Quintanilla‐Sanchez Carolina , Salcido‐Montenegro Alejandro , Nagaraj Siranjeevi , Leroy Karelle ,   Alzheimer and other tauopathies research group
Published: December 2023

AbstractBackgroundSeveral dysregulated miRNAs have been reported in Alzheimer’s disease mice models. However, not all may be relevant for human cases. Therefore, a compilation and evaluation of the evidence is required, before choosing miRNAs as biomarker candidates or treatment targets.MethodThe current review is being done following PRISMA guidelines. The search in MEDLINE yielded 3952 results. After “abstract and title screening”, 319 articles passed to “full text” screening. Currently, 139 studies are being reviewed, evaluating mice model, dysregulated miRNA, brain region, miRNA extraction and analysis method, and their predicted/proven targets. Only peer review articles of in vivo mice models are being included.ResultThis is an ongoing review and the first one of its nature. From a total of 90 different dysregulated miRNAs, 4 are the most consistent in the literature: miR‐146a, miR‐181, miR‐155 and miR‐200. The expression of miR‐146 is upregulated in the hippocampus of amyloid beta (5XFAD, APPswe/PS1L166P, APPswe/PSEN1dE9) and tau models (Tau22 and 3xTg‐AD for both of them). In general, this dysregulation is observed since early stages (3‐4 months) in transgenic mice, but also in 16.6 months‐old WT animals (1‐5). Parallelly, 2 studies confirmed increased levels of miR‐146 in the blood of MCI patients and in the hippocampus of AD patients (2,4). miR ‐155 is consistently upregulated in the hippocampus and cortex of APP and tau transgenic models, as well in AD patients (3,4,6). miR‐181a is overexpressed in the hippocampus of transgenic and old WT mice (2,7), whereas miR‐181c is downregulated in the APP/PS1 and SAMP8 mice (8,9). Finally, miR‐200 is downregulated in the hippocampus of 3‐9 months‐old APP/PS1 and SAMP8 mice (10,11), while an upregulation was observed in 10 months‐old Tg2576 mice (12). All of these studies validated their results with qRT‐PCR.ConclusionmiR‐146, miR‐181a and miR‐155 are consistently upregulated, whereas miR‐181c and miR‐200 are downregulated in the brains of different transgenic and non transgenic AD mice models at different ages. This data suggest further studies of these candidates miRNAs, as they are validated in human samples as well. However, a more robust conclusion can be made after assessing all the reported miRNAs.
Subcutaneous (SC) lecanemab is predicted to achieve comparable efficacy and improved safety compared to lecanemab IV in early Alzheimer’s disease (AD)
Authors: Willis Brian A. , Penner Natasha , Rawal Sumit , Aluri Jagadeesh , Reyderman Larisa 
Published: December 2023

AbstractBackgroundLecanemab is an anti‐amyloid antibody that has been approved by the United States FDA for treatment of early AD under the accelerated approval pathway. Lecanemab 10 mg/kg every 2 weeks (Q2W) intravenously (IV) was shown in the CLARITY‐AD trial to slow the progression of AD. Pharmacokinetic/pharmacodynamic (PK/PD) modeling demonstrated that lecanemab average steady‐state concentrations (Cav,ss) are associated with amyloid plaque lowering and efficacy, while maximum steady‐state concentrations (Cmax,ss) are correlated with the incidence of amyloid‐related imaging abnormalities‐edema (ARIA‐E). SC lecanemab is being developed to improve patient convenience and safety profile by lowering the rate of ARIA‐E.MethodTwo single‐dose studies (NCT05045716 and NCT05533801) were conducted in healthy subjects to establish absolute bioavailability of a drug‐in‐vial (SC vial) formulation and to compare PK of SC vial and autoinjector (AI). In the first study, subjects were administered 10 mg/kg IV Q2W or 700 mg SC vial once‐weekly (QW) lecanemab (n = 30/arm). In the second study, subjects were administered 720 mg SC vial or 720 mg AI lecanemab (n = 80/arm). Serum lecanemab concentrations were measured for 50 days, and noncompartmental PK parameters were calculated.ResultThe absolute bioavailability of SC vial was 49.7% (90%CI:43.5‐56.8). Cmax following SC vial was 22.8% of IV (59.8 vs 262 µg/mL). AI had 20% higher AUC than SC vial (20900 vs 17400 µg.h/mL). PK modeling demonstrated that Cav,ss following 720 mg QW, administered by SC vial or AI, is similar to 10 mg/kg IV lecanemab. Cmax,ss following SC vial or AI will be approximately half that of IV. Based on these results, exposure‐response models predict similar amyloid plaque lowering and efficacy for IV, SC vial and AI. ARIA‐E rates are expected to be reduced with SC formulations (APOE4 carriers, IV:14.0%, SC Vial:4.8%, AI:5.6%; APOE4 noncarriers, IV:5.4%, SC Vial:1.7%, AI:2.0%).Conclusion720mg QW using SC vial or AI results in comparable Cav,ss to 10 mg/kg Q2W IV lecanemab and is expected to have similar amyloid plaque lowering and efficacy. SC lecanemab has the potential to substantially lower incidence of ARIA‐E compared to IV. Ongoing studies will confirm the safety and efficacy profiles of SC lecanemab.
Burden of caregiving to people living with dementia in low‐and middle‐income countries in Asia: a systematic review
Authors: Tran Anh Tuan 
Published: December 2023

AbstractBackgroundHome‐based care is the primary type of care given to people with dementia in Asian countries due to cultural perceptions and the limited formal dementia care support. Long‐term caregiving can lead to a high caregiver burden, further strained by limited access to caregiver support in less developed countries. It is essential to assess the level of caregiver burden and its factors in such settings to understand the impact on caregivers’ well‐being and quality of life. Aim: To synthesize the evidence on the level of caregiving burden and its factors among caregivers of people with dementia (PWDs) in LMICs in Asia.MethodsSystematic review was conducted based on the PRISMA protocol. 71 eligible studies were identified from MEDLINE (Ovid), EMBASE (Ovid), PsyINFO (Ovid), CINAHL Plus (EBSCOhost), Cochrane Collaboration, Web‐of‐Science, and Scopus and from 2000 to 19 August 2022. Each article’s methodological quality was assessed with an appraisal checklist developed by the JBI.ResultsA total of 71 studies (12,424 caregivers) were identified from 09 LMICs (China, India, Iran, Malaysia, Pakistan, Philippines, Thailand, Turkey, and Vietnam). The caregiver burden was assessed using 10 tools, the commonest being Zarit Burden Interview (ZBI, n = 41) and Caregiver Burden Interview (CBI, n = 17). The prevalence of caregiver burden using ZBI ranged from 9.47% in Thailand to 64.57% in India. The prevalence of the burden of caregiving ranged from 25.00% in Thailand and 68.66% in China, as assessed by CBI. There were various patient characteristics (including severity and duration of dementia, Mini‐mental State Examination (MMSE) score, BPSD severity etc.) and characteristics caregiver characteristics (age, gender, education level, employment status, income, religion) associated with the burden of caregiving. Social support, positive aspects of caregiving (PAC), resilience, and intrapersonal resources were some of the moderating factors associated with caregiver burden. A meta‐analysis is still being attempted based on the homogeneity of the results.ConclusionCaregivers of people living with dementia in LMICs in the Asian region have a high burden of caregiving. There should be targeted interventions to alleviate the caregiver burden and to improve the quality of life of carers of people living with dementia in such countries
Prediction of short‐term MCI‐to‐AD conversion using multi‐modal neuroimaging features, and demographics
Authors: Kim Regina EY , Lee Min‐Woo , Choe Yeong Sim , Yang Hyeonsik , Lee Ji Yeon , Yong Jung Hyeon , Kim Donghyeon , Lee Minho , Kang Dong Woo , Jeon So Yeon , Son Sang Joon , Lee Young‐Min , Lim Hyun Kook , Kim Hye Weon 
Published: December 2023

AbstractBackgroundMulti‐modality data can achieve better classification and regression performance than the use of only the single modality data. For this reason, we observed the potential of multiple data for early dementia conversion.MethodIn this study, the conversion group was defined as cases where MCI patients converted to dementia between 2 and 4 years, and T1‐weighted, T2 FLAIR, and Amyloid PET images were acquired from 4 domestic hospitals and ADNI. 114 volume features, WMH volume, and Fazekas scale were acquired from T1, T2 FLAIR images, and SUVR was calculated from amyloid PET. In addition to image features, age, sex, MMSE, and ApoE genotype were used. The 206 cases dataset was divided in a stratified way into a training set (80%) and testing set (20%), keeping the sample percentage of each class in both sets. We wanted to check the performance of each model on our dataset. The models used for this purpose were Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Linear Regression Classifier (LR), Gradient Boosting Model (GBM), Extreme Gradient Boosting (XGB). We trained and tuned each model, set up a grid search through hyperparameters to select a model that generalized well. The metrics used to evaluate model performance were BA, SE, SP, and AUC.ResultTable 2 and 3 present the obtained results of all ML models for the two‐class classification task using 10‐fold cross‐validation and testing. In the case of 10‐fold cross‐validation, the model with highest mean BA was the SV model with 0.927. However, in the case of testing, we can see in Table 3 that the model with the highest BA, SE, and SP obtained was the GBM model, which was 0.917, 0.900, and 0.933, respectively. When ensemble was performed using the top3 models, BA, SE, SP, and AUC of the ensemble model were 0.917, 0.900, 0.933, and 0.963, respectively.ConclusionMuti‐modal neuroimaging features with minimal demographic information showed reliable results in predicting the converters and non‐converters between 2 and 4 years. This study suggests it could be utilized for future clinical trials to provide a high‐risk group for dementia conversion in advance.
Tau PET positivity comparison with visual rating, using automate quantitative method
Authors: Choe Yeong Sim , Ha Seunggyun , Kim Regina EY , Lim Hyun Kook , Kim Hye Weon 
Published: December 2023

AbstractBackgroundAlzheimer’s disease (AD) is highly related to brain amyloid and tau deposition. PET images evaluate these patterns of amyloid and tau proteinopathy noninvasively. Tau PET is less familiar than amyloid PET to clinicians and researchers, it may occur inter‐ and intra‐observer variability to determine abnormalities in visual rating. We purposed to find optimal thresholds of tau PET, compared with visual assessment, and suggest the most appropriate standard of tau PET assessment using the assist of automated quantitative analysis of PET.Method101 participants were enrolled in tau (18F‐flortaucipir), amyloid (18F‐flutemetamol) PET, and a three‐dimension T1‐weighted MR image study. Each regional SUVR was calculated using SCALE PET version 1.2 (Neurophet Inc., Seoul, Republic of Korea.). Each regions‐of‐interest (ROI)‐mean SUVR of reference region (whole cerebellum (CW) and cerebellar gray matter (CG)) and quantitative Tau PET positivity was compared with visual rating results of a nuclear medicine physician. We compared accuracy of tau PET positivity driven from 1) total Braak ROI, 2) a priori ROI, and 3) whole cerebral cortical mean SUVR above 1.65 (CW, CG) or 1.30 (CG).ResultThe highest accuracy was observed 0.832 with above 1.65 mean SUVR in a priori ROI using CG reference region. False negative (FN), where participants classified as negative with mean SUVR when visually positive (FN), was 26.7%. These FN participants were mostly identified as borderline patients who were categorized to Braak III/IV (3) or V/VI (24) in visual rating. All the participants classified as tau positive in the quantitative method were amyloid positive while 54.1% were in the tau negative group. Also, the patient group composed of AD and mild cognitive impairment (MCI) participants was 96.3% in positive, behalf 55.4% in negative group. Both visual rating and quantitative classification showed a significant difference between negative and positive groups (p <0.001).ConclusionVisual assessment may provide more sensitive outcome than quantitative estimation, i.e., mean SUVR. Our quantitative methods of tau PET showed high accuracy with visual assessment in the regions of early Braak stage, i.e., Braak I/II/III/IV. In the future, there needed to develop more sensitive cutoff values or quantitative methods for tauopathy deposition observation.
Protective Effects of AR1001 in Alzheimer’s Disease Models: Polypharmacological Mechanisms
Authors: Kang Byung Woo , Kumar Ashish , Song Dong‐Keun , Ha Jae‐Young , Choung Jai Jun 
Published: December 2023

AbstractBackgroundNitric oxide/cGMP/CREB signaling is critical for learning and memory processes and is disturbed by Abeta amyoid and in Alzheimer’s disease. AR1001 is an inhibitor of phosphodiesterase (PDE)‐5, which degrades specifically cGMP, thus having a potential to reverse AD pathology.MethodFor the in vivo study, AR1001 was intraperitoneally administered (4 mg/kg/day) to APP‐C105 mice for 4 weeks. Then after behavioral tests (Morris‐water maze and passive avoidance tests) were done, hippocampal levels of Abeta1‐42 and p‐Tau (S199/202) were determined. In the in vitro study using human neuroblastoma SH‐SY5Y cells and mouse hippocampal HT‐22 cells, the effects of AR1001 on Abeta1‐42‐induced cytotoxicity and intracellular signaling were examined.ResultAR1001 markedly improved cognitive functions in both Morris‐water maze and passive avoidance tests and reduced hippocampal Abeta1‐42 and p‐Tau levels in the APP‐C105 mice. In the in vitro experiments, AR1001 inhibited Abeta1‐42‐induced cell death, mitochondrial membrane potential derangement, and apoptotic markers (cleaved caspase‐3 and cleaved PPAR) concentration‐dependently. AR1001 reversed Abeta1‐42‐induced decrease in phosphorylated CREB (p‐CREB), and NGF/BDNF expression. Further, AR1001 blocked Abeta1‐42‐induced pSer9‐GSK3beta decrease and p25/p35 increase. Interestingly, compared with sildenafil, vardenafil and tadalafil, AR1001 was more effective in inhibiting glucocorticoid receptor (GR) dimerization and Wnt antagonist Dickkopf‐1 (Dkk‐1) expression, and in enhancing Wnt3a expression.ConclusionAR1001 had protective effects both in vivo and in vitro models of AD. Furthermore, AR1001 showed more pronounced activity in the modulation of GR and Wnt signaling among the various PDE‐5 inhibitors. It is suggested that the multimodal activity of AR1001 contributes to the therapeutic effects of AR1001 observed in the phase 2 clinical trial in AD patients (NCT03625622).
Classification of Alzheimer’s disease and frontotemporal dementia using electroencephalography and deep‐learning based approach
Authors: Sim Heedong , Yang Seok‐Jo 
Published: December 2023

AbstractBackgroundA dataset[1,2] of electroencephalography(EEG) of frontotemporal dementia(FTD), alzheimer`s disease(AD) patients and healthy control(HC) were classified using convolutional neural network(CNN) and evaluated its performancesMethodEEG dataset containing 88 subjects is downsampled and sliced into 10 seconds. Total dataset was divided into train, test and validation sets for each subject. CNN structure was used to extract temporal and spatial features from EEG and classify patients into 3 groups(FTD/AD/HC). Cross entropy loss was calculated to train the model using adam optimizer. Classification performances were measured to validate model such as accuracy, confusion matrix, AUC(area under curve), F1 Score.ResultAfter training, validation accuracy was 95.35% and macro‐averaged AUC was 0.9962. F1 score was 0.9469 and the minimum validation accuracy measured among subjects was 57.14% in FTD group. For AD group average accuracy was 92.79%, and for FTD group average accuracy was 97.52%. Average accuracy of healthy control group was 95.26%.ConclusionWe implemented CNN model that can classify EEG of dementia patients with very high performances without expert‐designed feature engineering process. These results imply that deep learning based approach is most promising for EEG‐based patient classification.