bluuebunny/crossref_metadata_2025_split huggingface dataset:
SAGE‐718 in Patients With Mild Cognitive Impairment or Mild Dementia due to Alzheimer’s Disease: Results From the Phase 2 LUMINARY Study
Authors: Koenig Aaron , Malhotra Shishuka , Wald Jeffrey , Petrillo Jennifer , Paumier Katrina , Johannensen Jason , Li Sigui , Quirk Michael , Freitag Emily , Doherty Jim 
Published: December 2022

AbstractBackgroundCognitive impairment has a deleterious impact on patients with AD and their caregivers; new therapies are needed. SAGE‐718, a novel N‐methyl‐D‐aspartate receptor positive allosteric modulator, is being investigated for the treatment of cognitive impairment in patients with neurodegenerative diseases such as Alzheimer’s disease (AD). The LUMINARY Study is designed to evaluate the safety/tolerability of SAGE‐718 and its effects on cognitive symptoms in patients with AD.MethodLUMINARY (NCT04602624) is an open‐label, Phase 2 study evaluating SAGE‐718 (3mg QD for 14 days) in patients with AD. Patients (aged 50‐80 years) with Montreal Cognitive Assessment (MoCA) scores of 15‐24 were included. Treatment‐emergent adverse event (TEAE) incidence through Day 28 (primary endpoint), other safety outcomes (secondary endpoints), and cognitive and functional assessments were analyzed.ResultTwenty‐six patients (69.2% female; mean age 67 years), with a mean±SD MoCA of 20.7±2.61 were enrolled. Most patients (23/26) had a global Clinical Dementia Rating score of 0.5. Eight TEAEs were reported in 7 (26.9%) patients; all were mild/moderate; 6 were treatment related. No serious adverse events or deaths were reported. At Day 14, improvements from baseline were observed on multiple tests of executive functioning (Digit Symbol Substitution, Multitasking, One Touch Stockings, Spatial Working Memory, and 2‐Back tests) and learning and memory (Pattern Recognition Memory and Verbal Recognition Memory tests). Statistically significant MoCA improvement (+2.3 points vs baseline) was observed at Day 28. No changes in attention/psychomotor speed were observed. Functional assessments also captured notable improvement in some patients (Clinical Global Impressions Scales and Amsterdam Instrumental Activities of Daily Living Questionnaire), particularly on items measuring aspects of complex/higher order activities.ConclusionIn this study, SAGE‐718 was generally well tolerated and associated with cognitive and functional improvements in patients with AD. These results support further investigation of SAGE‐718 for the treatment of cognitive impairment associated with AD and other neurodegenerative diseases.
Concerted alterations of functional connectivity and WM integrity in relationship to early amyloid deposition
Authors: Lorenzini Luigi , Orso Beatrice , García David Vállez , Pontillo Giuseppe , Ingala Silvia , Haller Sven , Blennow Kaj , Frisoni Giovanni B , Chetelat Gael , Payoux Pierre , Martinez‐Lage Pablo , Ewers Michael , Waldman Adam , Ritchie Craig W. , Gispert Juan Domingo , Visser Pieter Jelle , Mutsaerts Henk‐Jan , Tijms Betty M. , Wink Alle Meije , Barkhof Frederik 
Published: December 2022

AbstractBackgroundEarly amyloid deposition results in functional and structural brain alterations in predementia stages of Alzheimer's disease (AD). However, how early functional and structural brain changes are related to each other remains unclear. Investigating the simultaneous disruptions of functional‐structural brain features within individuals in relation to amyloid deposition may increase our understanding of the neuropathological processes underlying AD.MethodWe included 648 non‐demented participants from the European Prevention for Alzheimer’s Dementia (EPAD) cohort vIMI baseline data release. The cut‐off for cerebrospinal fluid (CSF) amyloid‐β positivity (A+) was defined at <1000pg/mL (Elecsys assay). Functional connectivity was estimated with functional eigenvector centrality (EC) from the resting state functional MRI. White matter (WM) integrity was estimated with fractional anisotropy (FA), computed on diffusion MRI. Both measures were computed at the voxel level within the gray and white matter, respectively. First, we used linear regression models to investigate differences between A+ and A‐ participants within each modality separately, adjusting for age, sex, and site. In the whole group, we performed sparse canonical correlation analysis (sCCA) on functional and structural measures, identifying shared components between the two modalities. Individual sCCA scores were compared between amyloid groups using a generalized linear model (GLM). To evaluate this association in A+ individuals, the same sCCA analysis was performed normalizing the structural and functional matrices using the mean and SD from A‐ participants.ResultSample characteristics are provided in Table 1. Differences in voxelwise EC and FA between amyloid groups are shown in Figure 1. In the whole group, sCCA analysis showed that EC in the default mode, executive and cerebellar networks covary with FA in parietal, temporal and cerebellar areas (Figure 2). sCCA projections were differentially expressed according to amyloid status (Table 2). In A+ participants, stronger association of EC in the posterior DMN and cerebellum with FA in the splenium and brainstem were found (Figure 3).ConclusionUsing a data‐driven multivariate analysis, we identified concerted patterns of functional and structural changes that occur with amyloid deposition in the predementia stage. These results highlight the importance of multimodal assessment for early diagnosis.
Sex Differences in the Clinical Presentation of Prodromal Dementia with Lewy Bodies
Authors: Wyman‐Chick Kathryn A , Bayram Ece , Chiu Shannon Y , Patel Bhavana , Armstrong Melissa J , O'Keefe Lauren R , Cunningham Laura C , Barrett Matthew J 
Published: December 2022

AbstractBackgroundSex is an important biological variable which can influence the prevalence, clinical presentation, and progression of neurodegenerative diseases. Little is known regarding possible sex differences in prodromal dementia with Lewy bodies (DLB).MethodWe analyzed data from the National Alzheimer’s Coordinating Center’s Uniform Data Set (NACC database is funded by NIH U01‐AG016976). Annual visits occurred across 39 past/present Alzheimer’s Disease Research Centers (ADRC) between September 2005 and September 2021. We included non‐demented participants at baseline who were clinically diagnosed with DLB by ADRC staff at a subsequent visit. The presence of motor symptoms, visual hallucinations, REM sleep behavior disorder (RBD), cognitive fluctuations, and neuropsychiatric symptoms were assessed through clinical exam and informant responses 1–2 years prior to DLB diagnosis.ResultWe identified 160 participants with DLB (82.5% male). Male and female groups differed in age and ethnicity (Table 1).There were no differences in education, cognition, functional status, severity of neuropsychiatric symptoms, or number of visits prior to phenoconversion to DLB.RBD was more prevalent in men (58‐62% vs. 25–36% women). Cognitive fluctuations were also more common among men (33‐40% vs. 6–18% women). Men reported a higher prevalence of visual hallucinations 2‐years prior to dementia diagnosis (23% vs. 6% women), but visual hallucinations were more prevalent in women than men at the first visit with dementia diagnosis (54% women vs. 43% men). Motor slowing and parkinsonian gait were prevalent among both groups; however, tremor and rigidity were more common in men, while postural instability and falls were more frequent in women. Apathy was common in both groups; however, anxiety was more frequent in men, and women had a higher prevalence of depression. (Table 2). The groups did not differ in rates of 1 vs. 2+ core clinical features 1‐year prior to diagnosis (Table 3).ConclusionThis descriptive analysis appears to indicate there are sex differences in the prodromal presentation of DLB. Men may present with established core features earlier than women. Limitations include lack of pathological confirmation and the number of female and ethno‐racially diverse participants. Understanding differences in clinical presentations may inform diagnostic criteria and therefore improve clinical sensitivity.
Functional Annotations‐Informed Whole Genome Sequence Analysis Identifies Novel Rare Variants for AD in the Alzheimer’s Disease Sequencing Project
Authors: Lee Songmi , Shi Bin , Peloso Gina M , Wang Yanbing , Heard‐Costa Nancy , Lin Honghuang , Pitsillides Achilleas N , Sarnowski Chloé , Boerwinkle Eric , De Jager Philip L , Dupuis Josée , Seshadri Sudha , Wijsman Ellen M , DeStefano Anita L. , Fornage Myriam 
Published: December 2022

AbstractBackgroundIncorporating functional annotations improves power to identify rare variants (RV) in the analysis of whole genome sequencing (WGS) association studies. We incorporated Alzheimer’s Disease (AD)‐specific annotations based on partitioning heritability into the variant‐Set Test for Association using Annotation information (STAAR‐O) framework to identify RVs associated with AD in the diverse sample of the Alzheimer’s Disease Sequencing Project (ADSP).MethodWe performed WGS association analyses in a sample of 4,074 individuals (1,668 AD cases; 2,406 controls) sequenced as part of the ADSP Discovery Extension Phase. We first estimated heritability of all single nucleotide variants in functional annotation categories using the GCTA tool embedded in FunSPU. For each annotation category, a global weight was derived based on the partitioned AD heritability. Among weighted functional scores, we selected the top 12 to leverage the most informative functional annotations for incorporation into the STAAR‐O test.2 We performed agnostic region‐based association analysis using sliding windows, defined as 2kb in length with a skip length of 1kb. Association tests were performed with RV (minor allele frequency <1%), adjusting for sex, sequencing center, platform, study, 4 principal components, and a genetic relatedness matrix. Replication of significant associations was carried out in an independent sample of 10,083 individuals (5,717 AD cases, 4,366 controls) sequenced as part of the ADSP Follow‐Up Study and using the same analytical models.ResultOut of 2.65 million 2‐kb overlapping windows with a total minor allele count >10, two non‐consecutive windows on chromosome 17 were associated with AD (P<5×10−8) (Figures). Both windows associations were replicated in the independent sample (P = 0.003 and 0.016). The top variant of one significant window was rs534148850 (MAF = 0.0005, P = 5.55×10−9) located downstream of PLEKHM1P1, a pseudogene, and MIR4315‐2, a microRNA with predicted targets enriched in apoptosis pathways. The top variant of the other window was rs532055552 (MAF = 0.005, P = 6.20×10−9) located downstream of CEP112, a coiled domain‐containing protein involved in the regulation of gamma‐aminobutyric acid A receptor surface expression.ConclusionIncorporating AD‐relevant functional annotations to a powerful RV association framework, we discovered and replicated two novel genetic regions harboring RV associated with AD.
Frequent Cognitive Tests Increase Power for Alzheimer’s Disease Clinical Trials
Authors: Roach Jared C. , Hara Junko , Edens Lance , Rajbhandari Sophiya , Dill Lauren , Romansik Rachel , Rapozo Molly K. , Funk Cory , Jade Kathleen , Merrill David A. , Bramen Jennifer E. , Panos Stella E. , Porter Verna R. , Dye Richelin V. , Ganapathi Aarthi S. , Hodes John F. , Slyapich Colby B. , Glatt Ryan M. , Wong Claudia L. , Fortier Dennis , Shankle William R 
Published: December 2022

AbstractBackgroundMany clinical trials use performance‐based cognitive or functional test results as primary outcome measures. As few as two measurements, typically pre‐ and post‐intervention, can be used to define a cognitive trajectory. Increasing the number and frequency of assessments can increase power to test the primary hypothesis. However, increased assessment frequency may be expensive and burdensome to participants and staff. Frequent neuropsychological testing may also introduce a practice effect or learning effect. Trials should be designed with attention to the costs and benefits of more frequent assessments.MethodWe conducted two clinical trials and performed Monte Carlo simulations. The Montreal Cognitive Assessment (MoCA) and the MCI Screen (MCIS) were used to assess cognition; the Functional Assessment Staging Tool (FAST) was used to assess function. In addition to simulated data, we employed data from the Coaching for Cognition in Alzheimer’s (COCOA) and Precision Recommendations for Environmental Variables, Exercise, Nutrition and Training Interventions to Optimize Neurocognition (PREVENTION) Trials.ResultEffect sizes and measurement variability were similar between the two cognitive screens. More frequent assessments increased power to reject a null hypothesis The major factor driving this improved power was the influence of data from individuals withdrawing from or dropping out of the trial prior to trial end. More frequent testing captured more of the effect of the intervention over time in those individuals, and therefore, increased power for the entire study. The design of the MCIS with rotating word lists facilitated frequent testing without a learning effect. Hypothetically, a more frequently administered assessment might have less precision than a different assessment administered less frequently, and the unit costs of these assessments might differ. Under a wide range of parameters modeled in simulations, the advantages of more frequent testing outweighed decreases in the precision of individual assessments.ConclusionMore frequent testing should be preferred in clinical trials. Use of shorter and/or remote tests may facilitate such frequency, reduce costs, and increase safety during pandemics. In addition to gains in statistical power, dense measurements have other benefits. They may also reveal non‐linear trajectories and better enable longitudinal analyses such as time dependencies and dose‐response relationships.
Lifestyle and psychosocial changes in Mexican elders during the COVID‐19 pandemic
Authors: Martínez‐Galindo Joyce Graciela , Salinas‐Contreras Rosa Maria , Rocha Ana Sabsil Lopez , Kivipelto Miia , Mangialasche Francesca , Ortiz Ana Luisa Sosa 
Published: December 2022

AbstractBackgroundSocial and physical activity, the optimal control of cardiovascular disease are important factors to prevent mild cognitive impairment and dementia. During the COVID‐19 pandemic these factors could have negatively been affected, and it is crucial to identify and address such changes.Nearly two years into the pandemic for COVID‐19 in worldwide, psychological and behavioural consequences have been observed in the population. Social distancing, mobility restraint and quarantine measures have raised concerns about lifestyles and mental health.MethodThis study included 50 urban‐Mexican elders cognitively health but with risk factors for dementia and mild cognitive impairment (age, mean: 72.6 ± 5.1, 54%, with an average schooling of 9.4 ± 4.3 years, 54% women), who responded face to face to a survey designed by the WORLD‐WIDE‐FINGERS‐SARS‐COV‐2 Initiative, which has the objective of measuring the direct and indirect effects of the pandemic in old age, focusing on changes in lifestyle and psychosocial factors, included frequency of physical activity, smoking, alcohol consumption and social activities before and after the pandemic quarantine.ResultA significant reduction in physical activity and attendance to social meetings was found. Other changes reported included decreased time spent with family, relatives and friends, increased sleep problems and augmented snacking but not increased consumption of fruits and vegetables. Participants also indicated an increased use of the Internet. Interestingly, even if people had less social interaction, they did not report increased feelings of loneliness.In general, few participants reported changes in physical health (16%) and quality of life (12%) but half of the sample (54%) feel that their memory was poorer than before the pandemic.ConclusionIn conclusion, we observed changes in lifestyle that can negatively affect mental health, as they involve factors linked to the occurrence of dementia or mild cognitive impairment. Therefore, strategies to mitigate the effects of these risk factors should be planned.
Plasma GFAP:NfL discriminates FTLD‐tau from FTLD‐TDP
Authors: Cousins Katheryn A Q , Shaw Leslie M. , Chen‐Plotkin Alice , Lee Eddie B , Trojanowski John Q , Van Deerlin Vivianna M , McMillan Corey T , Grossman Murray , Irwin David J. 
Published: December 2022

AbstractBackgroundBiomarkers are lacking that can discriminate frontotemporal lobar degeneration (FTLD) with tau (FTLD‐tau) from TDP‐43 (FTLD‐TDP). In FTLD patients with autopsy‐ or mutation‐confirmed pathology, we tested the glial fibrillary acidic protein (GFAP) to neurofilament light chain (NfL) ratio (GFAP/NfL) in plasma. We validated application of GFAP/NfL ratio in an independent sample of frontotemporal dementia (FTD).MethodsTraining sample was composed of 105 FTLD‐TDP and 44 FTLD‐tau participants, with pathology confirmed by either autopsy or pathogenic mutations (MAPT, C9orf72, GRN, TARDBP). Average accumulations of postmortem tau, TDP‐43, and amyloid across neocortical, limbic and brainstem regions were scored on a 0‐3 scale. Regression tested differences in analytes (log transformed) across FTLD‐tau and FTLD‐TDP, covarying for age, interval to death, and sex. Regression tested association of analytes (log transformed) with postmortem tau and TDP‐43 accumulation, covarying for postmortem amyloid‐β and plasma‐to‐death interval. Receiver operating characteristic (ROC) analyses and area under the curve (AUC) analyses were performed in patients with negligible secondary pathology. We defined an optimal cut‐point to discriminate FTLD‐tau from FTLD‐TDP using plasma GFAP/NfL. We tested the defined cut‐point in an independent validation sample of FTD patients with a clinical diagnosis of progressive supranuclear palsy (PSP) with likely tau (FTD‐tau; n=33) or diagnosis of amytrophic lateral sclerosis (ALS) with likely TDP‐43 (FTD‐TDP; n=35).ResultsPlasma GFAP was lower in FTLD‐TDP than FTLD‐tau (β=‐0.24, p=0.021) with small effect size (partial η2=0.082). Plasma NfL was higher in FTLD‐TDP than FTLD‐tau (β=0.57, p=1.0e‐05) with medium effect size (partial η2=0.24). GFAP/NfL was significantly lower in FTLD‐TDP than FTLD‐tau (β=‐0.82, SE=0.13, p=2.2e‐09) with large effect size (partial η2=0.39). Higher plasma GFAP was associated with higher postmortem tau accumulation (β=0.15, p=0.032). Higher levels of plasma NfL were associated with increased TDP‐43 burden (β=0.2, p=0.024). ROC revealed excellent discrimination by plasma GFAP/NfL in the autopsy/test sample (AUC=0.89). In the validation sample, plasma GFAP/NfL had good overall discrimination accuracy (AUC=0.85); there was 0.79 sensitivity and 0.77 specificity when applying the training‐derived GFAP/NfL threshold.ConclusionPlasma GFAP and NfL relate to pathological accumulation of tau and TDP‐43, respectively. The plasma ratio of GFAP/NfL is a promising candidate to discriminate FTLD‐tau from FTLD‐TDP.
Spiral Tracing in Alzheimer’s Disease: Less Precise but not Slower
Authors: Koppelmans Vincent , Silvester Benjamin , Ruitenberg Marit F. L. , Van der Geest Jos N. , Duff Kevin M. 
Published: December 2022

AbstractBackgroundIn the search for novel Alzheimer’s disease (AD) biomarkers, motor functional outcome measures are potential candidates, as motor impairments can precede cognitive dysfunction. Whereas most motor behavioral research in Mild Cognitive Impairment (MCI) and AD has focused on gait function, fine motor skill is affected as well. Here, we use a digitized version of the Archimedes spiral test to assess a variety of speed and accuracy metrics of fine motor function in MCI and AD compared to cognitively normal (CN) individuals.Method46 CN, 23 MCI, and 17 AD participants traced an Archimedes spiral 3 times using a digital pen tablet. Spiral outcome measures included: path length, movement time, tracing speed (average and variance), radial distance (i.e., total deviation from the spiral template; average and variance), number of template crossings, and return movements.ResultLinear regression analysis adjusted for age and sex showed that those with AD had significantly larger radial distance than CN (p = .0056, eta^2 = .096). No other significant group differences were observed (all p>0.14, all eta^2<.028).ConclusionResults indicate that individuals with AD are less precise when tracing an Archimedes spiral, despite adequate movement speed and steadiness. These results support a wider examination of fine motor dysfunction in AD as an inexpensive and easily collectable biomarker in AD.
A Machine Learning‐Based Approach for Classification of Alzheimer’s Disease and its Risk Prediction
Authors: Kasani Payam Hosseinzadeh , Kim Jung‐Kyeom , Kassani Peyman Hosseinzadeh , Kim Yeshin , Kim Jeong‐Ah , Park Chihyun , Yun Cheol‐Heui , Choi Seong Hye , Lee Sang‐Ah , Lee Seo‐Young , Jang Jae‐Won 
Published: December 2022

AbstractBackgroundDementia, a clinical syndrome with progressive neurodegenerative disease, is one of the inevitable aging consequences. Alzheimer’s disease (AD) is the most common cause of dementia. Accurate detection and classification of Alzheimer’s disease alongside its conversion from Mild Cognitive Impairment (MCI) to AD at an early stage is of significant clinical importance. However, a reliable diagnosis with a multi‐label classification approach remains a challenging task. This study aimed to measure the effectiveness of machine learning algorithm integrated with feature importance techniques to constitutes a multi‐label classification of AD prediction for clinical practice.MethodThe machine learning algorithms are validated using a nationwide cohort dataset, the Korean Brain Aging Study for the early diagnosis and prediction of AD (KBASE) to identify the most important risk factors, where a database is classified into three classes namely, normal cognition (NC, n = 91), mild cognitive impairment (MCI, n = 61), and Alzheimer’s disease (AD, n = 49). Applying eight state‐of‐the‐art supervised machine learning algorithms namely Support Vector Machine, Naive Bayes, XGBoost, Decision Tree, Logistic Regression, Random Forest, Bagging, and AdaBoost. A stratified 10‐fold cross‐validation was used for every train/test split and several performance measures, including accuracy, precision, recall and f1‐score were used to compare the classifier performances.ResultBased on the performance evaluation, the XGBoost classifier significantly outperformed all other models and achieved 82.55% accuracy followed by the Bagging classifier (81%). According to the XGBoost model, calculating risk factors to predict performance, the Consortium to Establish a Registry for AD (CERAD) subsets is among the highest ranked feature. It was intriguing that the parameters including job, job levels and retirement achieved a minimal, if any, impact in the final performance.ConclusionCollectively, the XGBoost as a Gradient Boosted Tree model can be considered as the best‐supervised machine learning algorithm for multi‐class AD classification. The model also provides a visualization of the contribution of the risk factors. The data mining approach integrated with machine learning models could achieve favorable outcomes in the accurate risk prediction and classification of AD that can be applied to the clinical setting as an assisting diagnostic tool.