bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dementia‐related psychosis in the United States: Estimated prevalence of patients treated with antipsychotics/mood stabilizers and methodology using third party claims database
Authors: Ramos Brett A. , Whitford Alex , Brain Cecilia 
Published: December 2020

AbstractBackgroundDementia affects approximately 8 million people in the US and about 2.4 million experience dementia‐related psychosis, such as hallucinations and delusions1‐15. However, the currently treated prevalence of psychosis in dementia is not well established. Currently there are no approved medications for dementia‐related psychosis and antipsychotics (APs)/mood stabilizers(MS) are commonly used to treat psychotic and behavioral symptoms.16‐19MethodInitially Anonymous Patient Level Data was used to identify patients prescribed APs and/or MS in the 1 year index period. Presence of dementia before psychosis was confirmed through a 1‐year look‐back period based on Dx or Rx. APs/MSs were enforced to be subsequent to the dementia diagnosis and/or treatment within the study period. The cohort was then analyzed to determine whether conditions such as schizophrenia and bipolar disorder were significantly more commonly observed than in the general population to exclude selection bias. Lastly the raw patient data was projected to the total universe using known coverage ratios for the data set and specific cohort.ResultAfter adjusting for non‐reporting sources, approximately 700,000 patients, who were identified by the methods criteria to have dementia and psychotic symptoms were prescribed APs based on the estimation. An additional 100,000 patients were prescribed MSs (but no APs) presumably due to dementia with psychosis.ConclusionApproximately 30% of persons with dementia suffer from psychosis and our findings suggest that only 33% of them are currently pharmacologically treated with APs and MSs, or ≤ 10% of about 8 million people with dementia. Further studies are needed to explore other potential therapies, usages, and treatments in this population.
Dementia‐related psychosis symptoms and impact from the patient and care partner (caregiver) perspective: An observational, prospective study to describe the patient experience
Authors: Brandt Teresa , Frangiosa Theresa , Biggar Virginia , Taylor Angela , Keller Bill , Abler Victor 
Published: December 2020

AbstractBackgroundThis study aimed to describe the person‐centered experience of symptoms and impact of dementia‐related psychosis (DRP) from a patient and care partner (CP) perspective.MethodsThis observational, non‐interventional, prospective study collected patient experience data to understand the impact of DRP in persons with various dementia subtypes. Qualitative interviews were conducted with persons or their CP who have a clinical diagnosis of all‐cause dementia with psychotic symptoms, recruited through physician referrals. The quantitative online survey was completed by persons with self‐reported DRP or their CP recruited by advocacy groups: UsAgainstAlzheimer’s and the Lewy Body Dementia Association. CP burden was beyond the scope of this study.ResultsSixteen individuals (1 patient, 15 CP) participated in the qualitative interview. The most commonly reported symptoms of DRP were visual hallucinations (88%), auditory hallucinations (69%), and persecutory delusions (56%). Participants described an impact of DRP on activities of daily living (75%), sleep (63%), family life (56%), and safety concerns (56%). The quantitative portion was completed by 212 participants (26 patients, 186 CP). DRP symptoms most frequently reported by patients were visual hallucinations (89%), auditory hallucinations (54%), and distortion of senses (54%); those reported by CP were paranoid delusions (76%), visual hallucinations (75%), and lack of trust for loved ones (52%). For patients with recent visual hallucinations, 61% of patients and 70% of CP reported experiencing this symptom on a weekly basis. When asked to rank the impact on the patient’s life, patients reported that visual hallucinations were the most impactful symptom (Figure 1) whereas CP reported paranoid delusions/false beliefs as most impactful, followed by visual hallucinations (Figure 2). Difficulty differentiating what is real from what is not real, increased anxiety, and effects on personal relationships were the most common types of impacts reported by both patients and CP. Most patients (>40%) were not receiving medications for DRP.ConclusionsPersons with DRP experience hallucinations and delusions that have a substantial impact on their lives. Here, patient experience data demonstrate that there is a need for treatments to reduce the symptoms and impacts of DRP.
Characteristics of currently available informant‐based tools to obtain collateral information for diagnosis and monitoring of cognitive disorders
Authors: Choudhury Samira 
Published: December 2020

AbstractBackgroundTo systematically review literature on informant based tools for the diagnosis and monitoring of neurocognitive disorders and make evidence based recommendations to clinicians and researchers as part of the 5th Canadian Consensus Conferences on the Diagnosis and Treatment of Dementia.MethodA systematic search was performed in MEDLINE, PsycINFO, Cochrane, and EMBASE databases. Limits used for searches were humans, English, and age above 45. Articles that validated the tools and or described their key properties were included. The original search yielded 358 articles. After removing duplicates and reviewing titles and abstracts, 167 remained and were reviewed in full text. Eventually 92 articles that described 16 informant based tools were included for final review.ResultBased on comprehensiveness and validity, 8 tools were found to be preferable for clinical and research use. AD8; Dementia Severity Rating Scale (DSRS); Everyday Cognition Scale (ECog); and the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) were selected for cognition and function and the Neuropsychiatric Inventory (NPI); Neuropsychiatric Inventory Questionnaire (NPI‐Q); Neuropsychiatric Inventory Clinician Rating scale (NPI‐C); and the Mild Behavior Impairment Checklist (MBI‐C) were selected for behaviour. Clinicians and researchers should choose a specific tool based on the need for comprehensive assessment versus desired efficiency depending on the setting.ConclusionThere are a number of evidence based tools that are appropriate to use in clinical care and research settings to assess cognition, behavior and function. Tools can be chosen based on specific needs and available resources.
CSF proteomic profiling of mild cognitive impairment individuals with suspected non‐Alzheimer’s disease pathophysiology
Authors: Delvenne Aurore , Gobom Johan , Tijms Betty M , Bos Isabelle , Verhey Frans RJ , Ramakers Inez HGB , Scheltens Philip , Teunissen Charlotte E , Vandenberghe Rik , Gabel Silvy , Popp Julius , Peyratout Gwendoline , Martinez‐Lage Pablo , Tainta Mikel , Tsolaki Magda , Freund‐Levi Yvonne , Lovestone Simon , Streffer Johannes , Blennow Kaj , Zetterberg Henrik , Visser Pieter Jelle , Vos Stephanie JB 
Published: December 2020

AbstractBackgroundSuspected non‐Alzheimer’s disease pathophysiology (SNAP) is a biomarker‐defined concept that encompasses individuals with Alzheimer’s disease (AD) neuronal injury but without amyloidosis. We have previously shown that 24% of mild cognitive impairment (MCI) individuals with SNAP will progress within 3 years to the AD dementia stage. Nonetheless, it remains unclear what explains the MCI‐SNAP biomarker profile and whether it reflects an early stage of AD, atypical AD or non‐AD pathology. We aimed to investigate the underlying pathophysiology of MCI‐SNAP using proteomics in cerebrospinal fluid (CSF).MethodWe included 206 individuals from the European EMIF‐AD MBD study and Maastricht BB‐ACL study. Based on CSF Aβ1‐42 (A) and p‐tau (T), individuals were classified as cognitively normal with normal A and T (CN, n=100), MCI with normal A and abnormal T (MCI‐SNAP, n=32), and MCI with abnormal A and T (MCI‐AD, n=74). We centrally generated CSF proteomic data for 1761 proteins using tandem tag mass spectrometry. We compared protein concentrations between groups using ANOVA adjusted for age and sex (1/3 observations per group minimum). We performed Gene Ontology enrichment analyses with false discovery rate to uncover the biological pathways of MCI‐SNAP. We investigated the expression profile of proteins in fetal/mature astrocytes, neurons, oligodendrocytes, microglia and endothelial cells.ResultSample characteristics are presented in Table 1. In MCI‐SNAP, we found 83 decreased and 28 increased proteins compared to CN and 177 decreased and 21 increased proteins compared to MCI‐AD. In MCI‐AD, we found 13 decreased and 193 increased proteins compared to CN. Proteins with decreased levels in MCI‐SNAP compared to both CN and MCI‐AD (n=55) were enriched for extracellular matrix, hemostasis, immune system, proteolysis and lysosome (Figure 1). A high percentage of these decreased proteins in MCI‐SNAP are expressed by mature astrocytes (51%) and endothelial cells (49%). Proteins increased in MCI‐SNAP and MCI‐AD compared to CN (n=17) were related to cytoskeleton organization and mainly expressed by neurons (88%).ConclusionOur results show that the pathophysiology of SNAP is distinct from that of AD. Further studies are needed to clarify the role of extracellular matrix, hemostasis, immune system, proteolysis and lysosome processes in SNAP.
AR1001 ameliorates Alzheimer’s disease pathology and symptoms by multi‐mechanisms
Authors: Kang Byung Woo , Kim Fred , Choi Yoon Pyo , Lee Younje , Kwak Da Eun , Shin Jisu , Kim YoungSoo , Choung Jai Jun , Rhee Jinseol 
Published: December 2020

AbstractBackgroundThe accumulation of amyloid‐b (Ab) in the brain is the primary pathological hallmark of Alzheimer’s disease (AD). Aggregated Ab is associated with cytotoxicity by a variety of mechanisms leading to neuronal apoptosis and synaptic impairments. Here, we present experimental evidence showing that AR1001, a highly selective and potent phosphodiesterase 5 (PDE5) inhibitor, acts as a polypharmacological agent for the treatment of AD by promoting neuronal cell survival and inhibiting Ab accumulation.MethodThe in vitro toxic effects of Ab were examined using the human neuroblastoma derived SH‐SY5Y cell line and mouse hippocampal neuronal HT‐22 cell line. Cell‐free Thioflavin T (ThT) fluorescence assay and SDS‐PAGE with PICUP (Photo‐Induced Cross‐Linking of Unmodified Proteins) of Ab plaques were employed to test inhibition of Ab aggregate formation and reduction of preformed oligomers and fibrils. To identify transcription factors responsible for inhibitory effects of AR1001 on Amyloid Precursor Protein (APP) / b‐site APP cleaving enzyme 1 (BACE1) expression, the Transcription Factor Profiling Plate Array was used to monitor the activation/inhibition of various transcription factors. 5XFAD transgenic mice were used to evaluate Ab42 induced neurodegeneration and amyloid plaque formation. NSE/APP‐C105 transgenic mice were used to test spatial and learning memory in the Morris water maze test and the passive avoidance test.ResultAR1001 markedly increased cGMP levels and activated cGMP/cGMP‐dependent protein kinase/cAMP responsive element‐binding protein (CREB) cascade in Ab‐treated SH‐SY5Y cells, promoting neuronal cell survival. In Ab‐treated HT‐22 cells, downregulation of Glucocorticoid Receptor (GR) transcriptional activity by AR1001 was responsible for reduced expression of APP, BACE1 and Dickkopf‐1 (Dkk1). In addition, AR1001 disrupted pre‐formed Ab42 fibrils in vitro. Interestingly, AR1001 reduced deposition of Aβ plaques in 5XFAD transgenic mouse brain by activating the autophagic process. Finally, AR1001 significantly improved memory functions in NSE/APP‐C105 mice.ConclusionOur findings strongly support that AR1001 has benefits against the accumulation of Ab and cognitive deficits in AD mouse models. We have demonstrated that AR1001 serves as a polypharmacological agent to inhibit PDE5 activity, promoting cell survival, and downregulate GR activity, reducing APP/BACE1 expression.
Longitudinal increase in depressive symptoms in relation to neurodegeneration in clinically normal older adults: Findings from the Harvard Aging Brain Study
Authors: Premnath Pranitha , Marshall Gad A. , Buckley Rachel F. , Donovan Nancy , Jacobs Heidi I.L. , Amariglio Rebecca , Vannini Patrizia , Chou Hsiang‐Chin Lori , Properzi Michael J. , Quiroz Yakeel T. , Rentz Dorene M. , Johnson Keith A. , Sperling Reisa A. , Hanseeuw Bernard , Gatchel Jennifer R. 
Published: December 2020

AbstractBackgroundEvidence suggests that depressive symptoms may be early behavioral changes preceding the development of Alzheimer’s Disease (AD). However, the neurobiological trajectory of depressive symptoms in reference to neurodegeneration of the hippocampus—a structure implicated in both mood and memory regulation—remains poorly understood in preclinical AD. In the current study, we examined the impact of baseline hippocampal volume (HV) both independently, and interactively with amyloid‐ß, on longitudinal change in depressive symptoms in clinically normal (CN) older adults.Method270 Harvard Aging Brain Study participants (age:73.5 ±6.1; 58% females) completed annual assessment with the 30‐item Geriatric Depression Scale (GDS) (average follow‐up=4.28 annual visits). All participants were cognitively unimpaired without clinically significant depression at study entry (mean GDS=3.09±2.86; range: (0‐12)). All underwent structural MRI scans and amyloid‐ß (11C‐Pittsburgh Compound B) PET at study year 1. In the primary linear mixed model, the association between dependent variable GDS (for each annual study visit) and HV was examined, with a random intercept and slope for each participant, covariates: age, sex, and education; and the interaction of predictors with time. In a secondary model, HV X cortical amyloid, X time was additionally included as a predictor.ResultIn the primary model, lower baseline HV (ß=‐0.0006; t=‐1.909; 95 % CI (‐1.2 x 10‐3, 1.7 x10‐5); p=0.057), but not its interaction with time (HV X time:(ß =‐0.000; t=‐1.12; 95% CI (‐7x10‐5, 6x10‐5); p=0.903) was marginally associated with higher GDS. In the secondary model, baseline HV and cortical amyloid were not interactive predictors of longitudinal GDS (HV X cortical amyloid X time: (ß =‐0.0001; t=‐0.90; 95% CI (‐4x10‐4, 0.0002); p=0.364).ConclusionLower baseline hippocampal volume is marginally associated with greater depressive symptoms; however, it did not independently, or in synergy with cortical amyloid, predict worsening depressive symptoms over time. These preliminary findings suggest that hippocampal atrophy may not be a predominant indicator of accelerating depression in CN older adults. Of note, our sample was non‐depressed at baseline. Future work in samples with non‐restricted behavioral symptoms and incorporating longitudinal AD biomarkers is needed to better understand neurobiological trajectory and identify targets for treatment at the earliest stages of AD.
Blood‐based detection of early‐stage Alzheimer using multiomics and machine learning
Authors: Souchet Benoit , Michaïl Alkéos , Billoir Baptiste , Mouton‐Liger Francois , Fortea Juan , Lleó Alberto , Paquet Claire , Braudeau Jerome 
Published: December 2020

AbstractBackgroundThe diagnosis of AD is based on cognitive symptoms, CSF assays and PET imaging. A blood test capable of detecting patients suffering from AD, at a prodromal or even pre‐symptomatic stage, will reduce the invasive and costly diagnostic tools currently used and, would allow an earlier clinical intervention. Here we assess the performance of a multiomics blood test using Machine Learning to combine metabolomic and proteomic biomarkers, pre‐identified in a brand‐new rat model.MethodBy sampling the plasma of a non‐transgenic animal model successfully reproducing the continuum of Alzheimer's disease progression at the brain level (Audrain et al., 2017), we identified the 105 most informative biomarkers using Artificial Intelligence. Then we analyzed the behavior of these biomarkers in 232 human plasma samples collected up to 15 years before the dementia. Three independent cohorts were used: two with the sporadic form of AD and one with Down Syndrome individuals. For each sample the 105 pre‐identified biomarkers (proteins, metabolites) were analyzed by global mass spectrometry. Using Artificial Intelligence, we identified the 25 best‐in‐class biomarkers in humans. Then we developed a neural network based on these 25 biomarkers to detect AD patients from the pre‐symptomatic phase.ResultAmong the 25 biomarkers, 13 are proteins and 12 are metabolites. None of these biomarkers are produced by the brain. They are produced or regulated by peripheral organs. The neural network identify Alzheimer’s patients (including pre‐symptomatic, prodromal and demented patients) from Non Alzheimer Individuals (Healthy controls and patients suffering from a neurodegenerative disease excluding Alzheimer) with 100% sensitivity and 99% specificity on a 5‐folds cross validation. Based on non‐linear and non‐monotonic biomarker progression, the neural network can identify undemented AD patients, including pre‐symptomatic and prodromal patients, against demented patient, with 99% overall accuracy.ConclusionIn our retrospective, multi‐center study, we achieved high accuracy (>99%) for Alzheimer’s from pre‐symptomatic phase. Proteic and metabolomic signals are required for an effective early detection. These results strongly suggest that the use of non‐brain‐produced biomarkers improves sensitivity and specificity of early detection.
The interrelationship between medial temporal atrophy and APOE4 biomarker: A comparison between Hispanics and white non‐Hispanics
Authors: Garcia Patricia , Mendoza Lisandra , Padron Dileanna , Duarte Andres , Duara Ranjan , Loewenstein David A , Greig‐Custo Maria T , Barker Warren W , Rodriguez Miriam J 
Published: December 2020

AbstractBackgroundThe Apolipoprotein E (APOE) gene is a major risk factor in developing late‐onset Alzheimer’s disease (AD; LOAD; de Oliveira et al., 2017; de Oliveira et al., 2014). The relationships between E4 genotype, brain morphology and ethnicity may provide insights into mechanisms by which ethnicity is associated with risk for AD. Because most research on genetic biomarkers has been conducted with Caucasian samples, this study aimed to explore the relationship of E4 (+) status to regional brain atrophy among an ethnically diverse cohort (Hispanics and White non‐Hispanics [WNH]) that are cognitively normal (CN) or have Mild Cognitive Impairment (MCI). We hypothesized that E4 (+) would be associated with brain atrophy in both ethnic groups.MethodCross sectional analysis of 240 participants was conducted of which 80 (23.1%) were E4 (+) (mean age= 71.02, SD=7.11); mean education= 14.46 (SD=3.42); 64.9% (n= 48) of the cohort were female. When stratified according to cognitive functioning (CN, MCI), the E4 (+) cohort was comprised of 32 WNHs (46.4%) and 35 Hispanics (50.7%). Pearson correlations were conducted between E4 (+) and volumetric variables (normalized to intracranial volume) in medial temporal regions sensitive to AD pathology—hippocampus, entorhinal cortex (ERC), and parahippocampal cortex (PHC).ResultA significant negative correlation was found between E4 (+) with L‐PHC (r= ‐.352, p= .022) and R‐PHC (r= ‐.348, p=. 024) among the combined CN group. When stratified by ethnicity, a negative correlation was seen in CN‐Hispanics between E4 (+) and L‐PHC (r= ‐.593, p= .002). No significant correlations were identified for WNH in both clinical groups and for MCI‐Hispanics.ConclusionOur findings indicate that E4 (+) status is associated with medial temporal brain atrophy among cognitively normal individuals. When examining ethnic groups, these correlations were evident among CN‐Hispanics. At the MCI stage, when brain pathology begins, these associations were not as strongly evident. Our findings highlight the importance of studying biomarkers among culturally diverse groups. Given the projected growth of Hispanics in the US, ongoing efforts should aim at disentangling cultural and genetic variables and their contribution to AD pathophysiology.
Virtual reality–based mindfulness training, sensory activation and mental assessment in dementia care
Authors: Paletta Lucas , Schüssler Sandra , Kober Silvia Erika , Zweytik Elke , Steiner Josef , Andreu Jean‐Philippe , Fuhrmann Ferdinand , Pszeida Martin , Dini Amir , Grabher Anna , Taberhofer Anna , Staubmann Wolfgang L , Draxler Theresa L , Lampl Christina L , Mayr Marlene L , Kölbl Gertraud L , Wood Guilherme L 
Published: December 2020

AbstractBackgroundRecent studies underline the importance of cognitive reserve, which is supported by stress reduction, pleasure experience and meditation, for mental health. Mindfulness training is successfully applied to dementia and indicate a lasting positive effect on cognitive reserve, well‐being and motivation. The research project OpenSense investigated the potential of VR‐based intervention and assessment for dementia care in a proof‐of concept study. The VR‐based intervention was developed to foster mindfulness and sensory activation.MethodVR‐based intervention was applied in persons with dementia (PwD) with Alzheimer’s dementia (AD; n=12, age M=85.0 years, MMSE M=21.5) and healthy controls (n=12, age M=75.1 years, MMSE M=30) using 30 minutes of panoramic video‐based multi‐sensory experiences presenting stimuli that empower relaxation (body‐scanning, beach, forest) and activation (bakery, orchestra). EEG‐based alpha‐band signals (8‐12 Hz) associated with relaxation and inhibitory control were recorded before, during and after intervention and eye tracking was applied during intervention.ResultPre‐post EEG analysis showed significant increases in alpha power and brain connectivity for PwD with AD and controls (post > pre, p<.05). EEG baseline alpha power demonstrated higher values for healthy controls than for PwD (AD). Eye movement analysis demonstrated significant differences between PwD (AD) and controls: eye blink rate AD > controls, p=.004(**) from the observation of a 3 minutes video, and significant correlation (Rho=.607, p=.003(**); 3 minutes video) was achieved between eye movements and the Freiburg Mindfulness Inventory score.ConclusionThe potential of VR‐based intervention based on mindfulness and sensory activation is very promising: the study demonstrated significant increases of EEG alpha power and brain connectivity where PwD usually suffer from decline and gaze data acquired during intervention indicate potential for non‐invasive assessment for decision support. OpenSense anticipates numerous opportunities for novel VR‐based care services for empowering cognitive reserve, inducing sensory activation, raising awareness and motivation for self‐regulation, and as pervasive assessment tool.