bluuebunny/crossref_metadata_2025_split huggingface dataset:
Occurrence of sleep disorders, anxiety, and depression among patients with mild cognitive impairment and Alzheimer’s disease in diverse populations: A large nationwide electronic health record cohort study
Authors: Sánchez‐Soliño Olga , Yue Emma Xiaomeng , Boroje Isabella , Vinikoor‐Imler Lisa C 
Published: December 2023

AbstractBackgroundPatients affected with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) often experience anxiety, depression, and sleep disturbances. Some of the published studies reported a higher incidence of these disorders in minority populations. The objectives of this study were to characterize the occurrence of sleep disorders, anxiety, and depression among patients with MCI and/or AD, stratified by gender, age and race/ethnicity in the United States clinical practice setting.MethodA retrospective cohort study was conducted using electronic health records and insurance claims from the Optum® Market Clarity database containing data from more than 63 million people in the US insured through Medicare, Medicaid, and other insurance providers. The study included patients diagnosed, utilizing ICD diagnosis codes, with MCI and/or AD between January 2017 and September 2021. Prevalence of depression, anxiety, and sleep disorders, as well as associated medication prescriptions, were identified in the 12 months before and after MCI and/or AD diagnoses. The occurrence of these diagnoses and prescriptions was evaluated stratified by gender, age, and race/ethnicity.ResultA total of 197,346 patients were diagnosed with MCI (56.9% female, mean age of 70.0 [SD 14.8]) and 144,321 with AD (63.9% female, mean age of 79.3 [SD 8.2]) at the baseline. Overall, a large proportion of MCI and AD cases had depression (pre‐MCI diagnosis: 40.3%; post‐MCI diagnosis: 44.8%; pre‐AD diagnosis: 32.2%; post‐AD diagnosis: 38.3%), anxiety (pre‐MCI diagnosis: 36.2%; post‐MCI diagnosis: 40.2%; pre‐AD diagnosis: 26.4%; post‐AD diagnosis: 31.5%), and sleep disorders (pre‐MCI diagnosis: 36.2%; post‐MCI diagnosis: 39.3%; pre‐AD diagnosis: 23.2%; post‐AD diagnosis: 25.4%). Occurrences of depression and anxiety were highest among females. Among individuals with MCI and/or AD, the gap between the proportion experiencing depression and the proportion prescribed an antidepressant was greatest for African American males and females. This was not observed for anxiety and anti‐anxiety medications.ConclusionA large proportion of individuals with MCI and/or AD also had depression, anxiety, and sleep issues. Further understanding of the populations with these comorbidities and their associated prescriptions by different ages, sex, and races/ethnicities is important when supporting patients with MCI and AD.
Neuroinflammatory biomarkers in neurodegenerative disease: Insights from the ONDRI Cohort
Authors: Sumra Vishaal , Dilliott Allison Ann , Frank Andrew R , Lang Anthony E , Roberts Angela C , Troyer Angela , Levine Brian , Arnott Stephen R. , Tan Brian , Fischer Corinne E. , Marras Connie , Kwan Donna , Munoz Douglas , Tang‐Wai David F. , Finger Elizabeth , Rogaeva Ekaterina , Orange Joseph B , Ramirez Joel , Sunderland Kelly M , Zinman Lorne , Binns Malcolm , Borrie Michael , Masellis Mario , Freedman Morris , Montero‐Odasso Manuel , Ozzoude Miracle , Bartha Robert , Swartz Richard H. , Tartaglia Carmela ,   The ONDRI Investigators
Published: December 2023

AbstractBackgroundNeuroinflammation (NI) has been implicated in both the pathogenesis of and neuroprotection against neurodegenerative diseases (NDs)(Psenicka et al., 2021). Plasma glial fibrillary acidic protein (GFAP), and Neurofilament light (NFL) are measures of astrogliosis and neurodegeneration, respectively. Amyloid beta (Aβ)42/40 ratio (Aβ42 concentration to total Aβ concentration) below 0.068 is associated with AD pathology (Baldeiras et al., 2018). Neuroimaging‐based inflammatory biomarkers have been proposed, including free‐water diffusion (FWD)(Pasternak et al., 2009). Here we investigated FWD as a candidate biomarker for NI in AD compared to non‐AD dementia using Aβ42/40 ratio in a subset of data from the Ontario Neurodegenerative Disease Research Initiative (ONDRI).MethodFWD maps were generated in 370 subjects (126 non‐AD and 244 AD). MRI processing included ICVmapp3r for brain extraction and bias field correction, Synb0, Topup and Eddy for dMRI preprocessing and MATLAB for freewater mapping. Plasma Aβ42, Aβ40, GFAP and NFL were measured using the Simoa Human Neurology 4‐Plex E assay, and cognition was estimated using the Montreal Cognitive Assessment (MoCA). Linear regression was used to estimate the ability for FWD in the left (LcGM) and right (RcGM) cortical grey matter to predict GFAP, NFL and MoCA score in AD and nonAD based on Aβ42/40 threshold of 0.068.ResultFWD correlated with GFAP (LcGM; R = 0.4, p = 0.0013 and RcGM; R = 0.37, p = 0.0069), and MoCA total score (LcGM; R = ‐0.29, p = 0.001 and RcGM; R = ‐0.27, p = 0.001), but not with NFL across the whole group. This relationship was largely driven by the AD group wherein FWD predicted GFAP (LcGM: R = 0.12, p = 0.02, RcGM approaching significance R = 0.1, p = 0.06), and MoCA Total score (LcGM: R = ‐0.33, p<0.0001), RcGM: R = ‐0.25, p<0.0001). The nonAD group did not show this relationship. FWD did not predict NFL in the AD and nonAD group.ConclusionIn patients with Aβ42/40<0.068, suggestive of AD, FWD in cGM was more strongly related to GFAP than NFL, and predicted cognition, a pattern that was not observed in nonAD patients. Our results suggest distinct patterns of NI in AD compared with nonAD that can be detected with FWD and with a multi‐modal approach could further understanding of differences in pathophysiology across NDs.
A study of DNA methylation in the prefrontal cortex of heterozygous GBA1 mutant mice
Authors: Weymouth Luke Stephen , Imm Jennifer L , Sander Millie , Brown Jonathan T , Lunnon Katie , Smith Adam R. 
Published: December 2023

AbstractBackgroundEpigenome‐wide association studies (EWAS) of DNA methylation are being undertaken in Parkinson’s disease (PD) and Dementia with Lewy bodies (DLB) post‐mortem brain to identify novel disease mechanisms. However, there is considerable heterogeneity in clinical and neuropathological manifestation, as well as the contribution of post‐mortem factors, which can confound smaller studies. The heterozygous GBA1 (D409V/WT) mouse model of DLB presents α‐synuclein deposition, cognitive decline and cholinergic dysfunction. We have undertaken the first systematic investigation of DNA methylation in these mice to identify changes related to α‐synuclein deposition. We have also explored the effect of varying post‐mortem interval (PMI) on DNA methylation patterns, to allow us to better interpret human studies.MethodIn total we profiled DNA methylation using the Illumina Infinium Mouse Methylation BeadChip array in 58 mice, corresponding to 28 GBA1 D409V/WT and 30 WT mice aged 8‐23 months. Within each age group we divided mice into 5 further groups to also explore the effect of PMI: +0hrs, +24hrs, +48hrs, +72hrs & +96 hrs respectively. The mice were culled and those in the +0hrs group were immediately dissected with the prefrontal cortex being flash frozen and stored in the freezer. Mice assigned to other PMI groups were culled and stored at 4 degrees for the relevant PMI before dissection and flash freezing. DNA was extracted, bisulfite treated and then profiled for DNA methylation.ResultOf the 58 mice that were sourced it was ensured that D409V/WT mice and WT mice were balanced between the 5 PMI groups. We have investigated the effect of phenotype, neuropathology and PMI on DNA methylation levels using linear regression models.ConclusionTeasing apart the effect varying PMI has on DNA methylation in post mortem brain samples will be a great aid when analysing such data going forward in large human EWASs. We have collated an in vivo study cohort to investigate DNA methylation patterns in the heterozygous D409V/WT mouse compared to WT, and interrogated the effect PMI has on the quality of methylation data. More broadly our study of PMI will have relevance to interpreting the results of human EWAS of DLB and other dementias.
Validation of QyScore’s® fully automated quantitative image segmentation tools against expert manual gold‐standards
Authors: Gordon Elizabeth , Borrot Mathilde , Villa Luca , Jubault Thomas , Guizard Nicolas ,   Alzheimer’s Disease Neuroimaging Initiative (ADNI), and Organization for the Advancement of Structured Information Stand (OASIS)
Published: December 2023

AbstractBackgroundQuantitative imaging provides valuable information for early detection, disease progression and treatment response monitoring. Manual segmentation is the current gold‐standard, but is prohibitively labour‐intensive for large scale use, such as in clinical routine, and suffers from individual variability. Thus, there is a substantial unmet need for validation of automatic segmentation techniques that perform as accurately as this labour‐intensive manual gold‐standard.MethodThe validation cohort consisted of 50 individuals with multiple diagnoses, wide age range (18‐86 yrs), balanced for sex and acquired on multiple scanners for improved generalizability of quantification results (Table 1). Three expert neuroradiologists each manually segmented the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles on 3DT1 images using itk‐SNAP (http://www.itksnap.org/) software. A consensus expert segmentation was then derived using the STAPLE (Simultaneous Truth and Performance Level Estimation) algorithm (Wakefield et al., (2004)) for each region and compared with the fully automated segmentations produced using QyScore®, a CE‐marked and FDA‐cleared neuroimaging medical device. Performance was investigated using the Dice Similarity Coefficient (DSC) and concordance assessed with plotted linear regression.ResultMean, standard deviation and confidence intervals of the DSC demonstrated a high degree of agreement between the consensus manual gold‐standard and QyScore®’s automated segmentations (Table 2). Importantly, this agreement remained consistent following stratification by field strength, demonstrating generalizability to most clinical imaging centres. For 1.5T scanners the mean DSC was 0.81, 0.89, 0.79, 0.86, 0.94, 0.93 and 0.91 for the caudate, putamen, globus pallidus, thalamus, cerebellum, brainstem, and lateral ventricles respectively. For the 3T scanners, this was equivalent or marginally better at 0.84, 0.89, 0.82, 0.86, 0.94, 0.94, and 0.93 respectively. In addition, strong concordance was demonstrated between the volumes, expressed as a percentage of intracranial volume (%ICV), obtained by the automated medical device QyScore® and the consensus of the manual gold‐standard segmentation (Figure 1: Table 2).ConclusionQyScore® produces fast reliable automated segmentations with comparable accuracy to expert neuroradiologists. These findings support the implementation of QyScore® in clinical trials and in clinical routine to provide quantitative image analysis in support of diagnosis and monitoring of disease progression and treatment response.
International implementation of Cognitive Stimulation Therapy in Brazil, India and Tanzania: Findings from the CST‐International study
Authors: Fisher Emily , Mograbi Daniel C. , Ferri Cleusa P , Santos Raquel L , Naylor Renata , Brum Paula Schimidt , Bertrand Elodie , Benevides Pedro , Vaitheswaran Sridhar , Venkatesan Shreenila , Natarajan Nirupama , Krishna Murali , Chandra Mina , Shaji K S , Mkenda Sarah , Urasa Sarah , Walker Richard , Dotchin Catherine , Paddick Stella‐Maria , Walker Jess , Oliver Benedict , Stoner Charlotte , Spector Aimee E 
Published: December 2023

AbstractBackgroundThere is a pressing need for effective and affordable interventions to support quality of life and cognition in people with dementia. Many psychosocial interventions are developed but few are delivered at scale and in routine practice. Group Cognitive Stimulation Therapy (CST) is the only non‐pharmacological treatment recommended by the National Institute for Health and Care Excellence (NICE) to improve cognition and wellbeing in mild to moderate dementia, and its global implementation has been recommended by Alzheimer’s Disease International. CST has been culturally adapted for use in at least 36 countries, but this has not resulted in widespread delivery in routine practice.MethodsMethodology consisted of four phases 1) Exploring barriers to implementation through stakeholder engagement with clinicians, policy‐makers, people with dementia and caregivers; 2) Development of implementation plans for each country; 3) A mixed‐methods pre‐post study of CST in each country (total n = 103), exploring feasibility, acceptability, cost and outcomes including cognition and quality of life; and 4) Refinement of implementation strategies, with an aim of creating ongoing and sustainable CST programmes.ResultsImplementation plans were developed for each country based on stakeholder engagement. CST delivery took place virtually in Brazil and India due to the Covid‐19 pandemic. We will present the barriers and facilitators to CST implementation in different settings based on the Consolidated Framework for Implementation Research (CFIR), and reflect on the success of implementation strategies. These include subsidising cost and travel, establishing a caseload of participants to receive CST, promotion and marketing of CST groups, educating family caregivers on the role of psychosocial interventions and enlisting their endorsement, and developing a ‘train the trainer’ model for CST group leaders. We will discuss the feasibility and acceptability of in‐person and virtual CST in varied settings, drawing on quantitative and qualitative findings from the pre‐post study of CST.ConclusionIt is crucial to not only culturally adapt the content of a psychosocial intervention, but also tailor implementation strategies for unique settings, considering economic realities, health equity and healthcare access. Key next steps to widespread CST implementation are long‐term funding arrangements and endorsement from regional or local guidelines.
Comparison of clinicopathologic features of Limbic and Diffuse Transactive Response DNA‐binding Protein 43 pathology in Alzheimer’s disease neuropathologic spectrum
Authors: Carlos Arenn Faye , Machulda Mary M. , Rutledge Matthew H. , Nguyen Aivi T. , Reichard Ross R. , Dickson Dennis W. , Petersen Ronald C. , Josephs Keith A 
Published: December 2023

AbstractBackgroundIncreasing evidence suggests that transactive response DNA‐binding protein 43 (TDP‐43) pathology in Alzheimer’s Disease (AD), or AD‐TDP, can be diffuse or limbic‐predominant. Understanding whether diffuse AD‐TDP has genetic, clinical, and pathological features that differ from limbic AD‐TDP could have clinical and research implications. We aimed to better characterize the clinicopathologic features of diffuse AD‐TDP and differentiate it from limbic‐predominant AD‐TDP pathology.MethodThree hundred sixty‐three participants were prospectively recruited and followed in the Mayo Clinic Study of Aging, Alzheimer’s Disease Research Center, and Neurodegenerative Research Group and died between May 1999 and August 2021. All underwent genetic, clinical, neuropsychologic, and neuropathologic evaluations. At postmortem evaluation, they showed Alzheimer’s disease neuropathologic changes and TDP‐43‐immunoreactive inclusions. The distribution of TDP‐43 pathology was assessed using the Josephs 6‐stage scheme. Participants with TDP‐43 stages 1‐3 were classified as Limbic, while those with stages 4‐6 were classified as Diffuse. Neuropathologically defined frontotemporal lobar degeneration cases were excluded. Multivariable logistic regression was used to identify clinicopathological features that predicted diffuse TDP‐43 pathology at postmortem. We hypothesized that the Diffuse group would show impairment in cognitive domains beyond episodic memory and more hippocampal sclerosis.ResultsThe cohort was 61% female and old at onset (median:76 years [IQR:70‐82]) and death (median:88 years [IQR:82‐92]). Fifty‐four percent (n = 197) were classified as Limbic and 46% (n = 166) as Diffuse. A “clinical” multivariate logistic regression fit to 85 participants with all clinical variables of interest showed that later age at onset (OR:1.09, 95%CI:1.01‐1.19;P = 0.03), longer disease duration (OR:1.17, 95%CI:1.02‐1.36;P = 0.03) and higher Neuropsychiatric Inventory scores (OR:1.14, 95%CI:1.01‐1.28;P = 0.03) all independently increased the odds of being Diffuse, while higher Trails Making Test‐B (OR:0.99, 95%CI:0.98‐0.99;P = 0.02) and Block Design Test scores (OR:0.91, 95%CI:0.85‐0.99;P = 0.023) independently decreased the odds of being Diffuse. A “pathology” multivariate logistic regression fit to 233 cases with all neuropathological data revealed that widespread amyloid‐β pathology corresponding to Thal phases 3‐5 all significantly decreased the odds of diffuse pathology by 80‐90%, while hippocampal sclerosis increased it sixfold (OR:6.18, 95% CI:2.93‐13.03;P<0.001).ConclusionsDiffuse AD‐TDP shows clinicopathological features that differ from Limbic AD‐TDP, which could have public health implications relating to treatment and research on AD and dementia.
The association between AD biomarkers and neuropsychiatric symptoms in subjective cognitive decline; the SCIENCe project
Authors: Trieu Calvin , van Harten Argonde C. , van Leeuwenstijn Mardou S. S. A. , Kroeze Lior A. , Ebenau Jarith L. , Verberk Inge M.W. , Sikkes Sietske A.M. , Verfaillie Sander C.J. , Giessen Elsmarieke , Teunissen Charlotte E. , van der Flier Wiesje M. 
Published: December 2023

AbstractBackgroundNeuropsychiatric symptoms (NPS) are increasingly recognized as non‐cognitive manifestation of Alzheimer’s disease (AD). However, the relationship between AD‐related pathology and NPS remains unclear. We aimed to investigate the cross‐sectional association between Alzheimer biomarkers and neuropsychiatric symptoms (NPS) in individuals with subjective cognitive decline (SCD) presenting at a memory clinic. In addition, we evaluated personality traits and education as potentially modifying factors of this association.MethodWe included 346 individuals with SCD (age: 62.9±7.8) from the SCIENCe cohort, including 92 amyloid positive participants (age: 66.4±7.0, Female: 44.6%, MMSE: 28.5±1.3) and 254 amyloid negative participants (age: 61.6±7.7, Female: 40.6%, MMSE: 28.6±1.3). Amyloid status was determined by amyloid PET or CSF biomarkers according to visual read or established cut points. Neuropsychiatric symptoms were assessed using the Geriatric Depression Scale (GDS; n = 282), the Center for Epidemiological Studies‐Depression (CES‐D; n = 225), the Hospital Anxiety and Depression Scale – anxiety subscale (HADS‐A; n = 225) and the Neuropsychiatric Inventory Questionnaire (NPI‐Q; n = 198). We used linear regression analyses, adjusted for age and sex, to assess the association between amyloid status and NPS. In secondary analyses, we tested for interactions between amyloid status and (1) neuroticism (Dutch Personality Questionnaire), (2) somatization, (Four‐Dimensional Symptom Questionnaire), and (3) educational level (Dutch Verhage scale).ResultAmyloid status was not associated with GDS, CES‐D, HADS‐A and NPI‐Q scores in univariable analyses. When we evaluated effect modification, we found an interaction between amyloid status and neuroticism in the association with NPI‐Q (p<0.05), indicating a positive association between amyloid status and NPS in participants with a low level of neuroticism (β: 2.56±2.24), and a negative association in participants with a high level of neuroticism (β: ‐4.09±3.23).ConclusionIn this cross‐sectional analysis in individuals with SCD, amyloid status was hardly associated with neuropsychiatric symptoms. In future studies, we focus on amyloid status as determinant of change in NPS over time.
Evaluating depressive symptoms and risk of cognitive decline using longitudinal electronic health records
Authors: Wang Liqin , Yang Richard , Laurentiev John , Gatchel Jennifer , Blacker Deborah , Amariglio Rebecca E. , Sperling Reisa A. , Zhang Yuqing , Zhou Li , Marshall Gad A 
Published: December 2023

AbstractBackgroundDepression is associated with increased risk of Alzheimer’s disease and related dementias (ADRD). Many population‐based and convenience sample studies have examined depression and depressive symptoms in this context. However, fewer studies employed electronic health records (EHR) to assess this relationship. Because depression might be under‐ or mis‐diagnosed, diagnostic codes alone may be insufficient. We aimed to assess whether prior depression diagnosis and/or depressive symptoms–derived from natural language processing (NLP) in the EHR–predicts future mild cognitive impairment (MCI)/dementia using a large EHR‐based dataset.MethodThe Massachusetts Electronic Health Records Aging Dataset (MEAD) is a retrospective cohort of patients who were ≥50 years old with normal cognition at their first encounter between 1/1/2000‐12/31/2005. Depression diagnoses and incident MCI/dementia were identified from structured encounter diagnoses and problem lists, and depressive symptoms from clinical notes using NLP. Follow‐up time was calculated from index date until date of cognitive diagnosis, death, loss to follow‐up or end of study (12/31/2021). We performed Cox proportional hazards models using age as the time scale to examine the association of depression and incident MCI/dementia, using the Fine‐Gray approach to account for competing risk of death.ResultThe MEAD cohort included 162,069 patients. Of those, 135,272 did not have depressive symptoms or a depression diagnosis at their first encounter, 14,985 had only depressive symptoms, 4,883 had only a depression diagnosis, and 6,929 had both depressive symptoms and a depression diagnosis. The incidence rate of MCI/dementia per 1,000 person‐year was 7.8 for patients without depressive symptoms or depression diagnosis, 10.0 for depressive symptoms only, 12.4 for depression diagnosis only, and 10.3 for both. Compared with those without depressive symptoms or depression diagnosis, the multivariable‐adjusted hazard ratios for incident MCI/dementia were 1.05 (95% confidence interval [CI] 0.99,1.11) for depressive symptoms only, 1.45 (95% CI 1.34,1.56) for depression diagnosis only, and 1.15 (95% CI 1.07,1.23) for both, respectively.ConclusionIn a large sample, diagnosis of depression appears to be a stronger predictor than depressive symptoms of all‐cause MCI/dementia. Further research is needed to understand the utility of reported depressive and other neuropsychiatric symptoms in clinic notes for predicting cognitive decline.