bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessment of Lifecourse Social Exposome in Brain Bank Decedents
Authors: Keller Sarah A , Powell W. Ryan , Buckingham William R. , Boone Brittney Lyons , DeWitt Amanda , Kramer Joseph A , Bendlin Barbara B , Kind Amy J. 
Published: December 2023

AbstractBackgroundThere is increasing evidence that the social exposome impacts the development of Alzheimer’s disease and related dementias (ADRD). The social exposome includes factors such as neighborhood disadvantage, and is thought to impact brain health across the lifecourse, particularly during sensitive life stages such as early childhood. Residential histories are required to assess dosage and timing of exposures, yet are rarely categorized and linked to brain bank cohorts. Our objective was to characterize the lifecourse neighborhood disadvantage across two Alzheimer’s Disease Research Center (ADRC) brain banks.MethodAfter abstracting publicly available residential data, we constructed residential histories using validated metrics of neighborhood disadvantage, nationwide Area Deprivation percentile rankings (ADI), constructed for each census timeframe from 1910‐present, to measure lifecourse social exposome time‐concordantly aligned to participant life‐year. ADIs were linked for all available years between birth and death, ranging from age 0 to 103. Greater ADI values denote living in higher neighborhood disadvantage. These ADIs were linked to the residential histories for each decedent and used to calculate two sensitive life stages: ages 0‐19 and 40‐60. A repeated measures one‐way ANOVA determined variance in ADI both within and between these sensitive life stages.ResultThis sample contained 1309 decedents from two brain. To date, 1230 decedents have been linked to at least one ADI timepoint, with an average of 81% (71‐88%) of all life‐course years accounted for via ADI linkage. The mean ADI for the 0‐19 period was 23.5 (SD = 22.3, 5.0‐36.9), while the 40‐60 period was 22.0 (21.6, 5.0‐34.0). A repeated measures one‐way ANOVA found a statistically significant difference in ADI between the two windows of time.ConclusionTissue banked samples are not typically characterized on social determinants of health. Here we show that determining lifecourse social exposomes are feasible using publicly available data. To our knowledge, this is the first time such characterization has been successfully performed. Efforts to link decedents to ADI timepoints are still ongoing, which will reduce missingness, enhance measurement precision, and allow for comparison between all life stages. This work provides a unique methodology for assessing the social exposome in relation to ADRD brain pathology.
Influence of education level on domain‐specific MOCA performance in the MarkVCID cohort
Authors: Thirion Marissa L. , Chatterjee Ankita , Klomhaus Alexandra , Tibbe Tristan , Vossel Keith , Kramer Joel H. , Hinman Jason 
Published: December 2023

AbstractBackgroundEducation level is a well‐recognized co‐factor for cognitive performance and a potential confounder in the application of cognitive evaluations in diverse populations. The Montreal Cognitive Assessment (MoCA) is a widely utilized screening tool for Mild Cognitive Impairment (MCI) composed of 6 domains of cognition: Executive Function (EFC), Attention and Concentration (AC), Language (LANG), Visuospatial (VIS), Memory (MEM), and Orientation (ORIEN). Education is currently accounted for globally by adding 1 point to the total MoCA score. This study considers the impact of education level on a domain‐specific scoring of the MoCA.MethodWe utilized longitudinal subject level data from one site within the MarkVCID Consortium (UCSF) composed of 578 subjects (59.5% female; 82.9% white), mean age 71.5 (+/‐ 8.7). Mean educational level was 15.3 (+/‐4.7) years. Within the cohort, 37 subjects lacked educational level and were omitted (final n = 541). Multivariate linear regression models were used to relate educational level with total and domain specific MoCA score performance at baseline while controlling for the influence of age, sex, and recognized MarkVCID biomarkers that impact cognitive performance including white matter hyperintensity (logWMH), fractional anisotropy (FA), free water (FW), and peak width of skeletonized mean diffusivity (PSMD).ResultsAn age‐, sex‐adjusted model replicated a significant association of education level with MoCA total score (p<0.001, b = .336). Among the MoCA domains, EFC was most affected by education level (p<.001, b = .412), followed by AC (p<.001, b = .352). Other MoCA domains were not significantly associated with educational level. Several MoCA domains, namely MEM and ORIEN, showed an association with MarkVCID biomarkers including WMH and FW (p‐values <.001). In regression models controlled for MarkVCID imaging biomarkers, educational level remained significantly associated with total and domain‐specific MoCA performance.ConclusionEducation does not equally contribute to all cognitive domains assessed by the MoCA, mostly affecting EFC and AC. These findings suggest that considering education level in a domain‐specific manner could provide a more accurate interpretation of the cognitive impairment.
Identifying frailty‐related clusters of Alzheimer’s and Vascular dementia: A multi‐modal data approach
Authors: Quach Jack , Nar Aditya , Maxwell Selena P , Motaghi Mahboubeh , Rockwood Kenneth , Duchesne Simon , Theou Olga 
Published: December 2023

AbstractBackgroundThe proportion of people living with dementia (PLWD) is increasing due to aging global populations. Frailty is a complex, multifactorial, and age‐related state of increased vulnerability commonly co‐occurring with Alzheimer’s disease (AD) and vascular dementia (VaD). Variability in frailty levels of PLWD motivates deeper investigation into dementia’s presenting characteristics. Machine learning techniques applied to large medical datasets can identify characterize disease phenotypes. Therefore, the aim of this study is to use unsupervised machine learning to discover frailty‐related clusters in AD and VaD.MethodWe will use data from the United Kingdom Biobank (UKBB), a large‐scale prospective cohort database which includes more than 500,000 participants aged 40‐69 years at baseline. The data include electronic health records, brain magnetic resonance imaging, polygenic risk scores, and frailty index scores. We will select features for clustering using principal component analysis. Clustering algorithms will include k‐means, affinity propagation, and latent class analysis. After cluster characterization, we will evaluate the association between clusters and prevalent AD (n = 2,634) and VaD (n = 1,664) using logistic regression models. Regression models will be stratified by sex and adjusted for age. Receiver‐operating curve will be plotted, and area‐under the curve will be compared using C‐statistics. This study is supported by the Canadian Consortium on Neurodegeneration in Aging Trainee Synapse Project Funds. Our data application has been approved by UKBB.ResultWe expect that the clustering algorithms will reveal several clusters of similar PLWD and allow evaluations based on shared patterns of regional brain atrophy, the degree of frailty, and polygenic risk. In addition, we also expect that clusters characterized by high frailty levels will be associated with prevalent AD and VaD.ConclusionFrailty is an important factor in the clinical evaluation of PLWD. The use of unsupervised machine learning techniques may reveal distinct high‐risk clusters that are related to prevalent AD and VaD. This work may improve early identification of PLWD and aid in the development of personalized care and therapy for people at risk of AD and VaD.
A Comparison of the Prevalence of Diabetes between Aging Rural and Urban Indians.
Authors: PANDEY SHRUTI , Lingegodwa Abhishek Mensegere , Malo Palash Kumar , Hameed Shafeeq K Shahul , Issac Thomas Gregor , Sundarakumar Jonas S. 
Published: December 2023

AbstractBackgroundDiabetes is one of the important modifiable risk factors for dementia. Its prevalence has been increasing rapidly in the past few decades across the world, particularly in India. However, among Indians, there are prominent lifestyle differences between rural and urban residents. Hence, understanding diabetes prevalence in these two distinct populations is essential to understand the differential risk for dementia in rural and urban Indians.MethodCross‐sectional (baseline) clinical data from two ongoing prospective cohort studies, namely Srinivaspura Aging, Neuro Senescence and Cognition (SANSCOG) study in rural India and Tata Longitudinal Study of Aging (TLSA) in urban India was utilized for the present study. A total of 6002 participants – 4913 rural (SANSCOG) and 1089 urban (TLSA) were included. Diagnosis of diabetes was made using self‐reported history and / or fasting blood glucose level of > 126 mg/dl. Chi‐square test of independence was used to assess the group differences between the rural and urban participants – both overall and gender‐wise and age group‐wise.ResultThe mean age of participants was 63.93 ± 9.56 years and 58.74 ± 9.88 years for the urban and rural cohorts, respectively. Prevalence of diabetes was significantly higher in urban than rural subjects – overall (33.6% vs. 19.3%, p<0.001) as well as among males (38.4% vs. 24.1%, p<0.001) and females (28.7% vs. 14.8%, p<0.001). Similarly, on age stratification, urban subjects had significantly higher prevalence than rural subjects in the age groups <65 years (28.1% vs. 18.3%, p<0.001) and ≥65 years (39.5%vs. 21.3%, p<0.001).ConclusionUrban subjects had significantly higher diabetes prevalence than rural subjects across both genders and age groups. In view of the association of diabetes with dementia risk, these findings could imply that urban Indians may be at higher risk for pathological cognitive impairment and dementia as compared to their rural counterparts. However, longitudinal follow of these participants with cognitive monitoring is required to establish a causal association.
Memory Concerns and Cognitive Change in a Diverse Cohort of Oldest Old Individuals: LifeAfter90 Study
Authors: Chen Nancy X , Corrada María M. M. , Kawas Claudia H. , George Kristen M , Lor Yi , Glymour M. Maria , Gilsanz Paola , Farias Sarah Tomaszewski , Mayeda Elizabeth Rose , Gradwohl Natalie M , Peterson Rachel , Whitmer Rachel A. 
Published: December 2023

AbstractBackgroundWhether subjective evaluations of cognition can accurately predict cognitive decline is not well understood in ethnically diverse populations nor oldest‐old. In a multi‐ethnic cohort of adults aged 90+, we aim to investigate the association between self‐perceived memory concerns and objectively measured cognitive change.MethodLifeAfter90 is an active longitudinal cohort study of Kaiser Permanente Northern California members ages 90+ with no prior dementia diagnosis. Verbal episodic memory (VEM) and executive function (EF) were assessed every 6 months between July 2018 and March 2022 using the Spanish and English Neuropsychological Assessment Scales (SENAS). At baseline, participants answered the question “Are you concerned that you have a memory or other thinking problem?” We evaluated associations between memory concerns and domain‐specific cognition at baseline and over time using linear mixed models with random intercepts and slopes, adjusted for baseline age, sex, race/ethnicity, education, interview mode (in‐person or phone), and practice effects. Sensitivity analyses stratified by 1) gender and 2) age (+/‐ 95 years).ResultAt baseline, participants (n = 975) had a mean age of 92.4 (SD = 2.3) years, 61% were women, 73% were racial/ethnic minorities, and 36% reported memory concerns (Table 1). Memory concerns were associated with worse baseline VEM (b = ‐0.17, 95% CI ‐0.27,‐0.07; Table 2) and EF (b = ‐0.08, 95% CI ‐0.15,‐0.01; Table 3), but not associated with rate of change for either domain. When stratified by gender, both men and women with memory concerns had worse baseline VEM (b = ‐0.21, 95% CI ‐0.36,‐0.06 and b = ‐0.15, 95% CI ‐0.27,‐0.02, respectively; Table 2). Additionally, men with memory concerns had worse baseline EF (b = ‐0.19; 95% CI ‐0.30,‐0.08; Table 3). Among participants younger than 95 years, memory concerns were associated with worse baseline VEM (b = ‐0.15, 95% CI ‐0.25,‐0.04; Table 2) and EF (b = ‐0.10, 95% CI ‐0.17,‐0.02; Table 3).ConclusionIn this diverse cohort of oldest‐old, those with subjective memory concerns had worse baseline VEM and EF. Although memory concern may suggest objective cognitive impairment, it is not associated with decline over the short term; (an average follow up time of 1.05 years). Additional follow up will identify how changes in memory concerns may be associated with cognitive change.
Correlation between cognitive status of patients and score of Story Memory test
Authors: Lee Yunji , Kim Hyunsook 
Published: December 2023

AbstractBackgroundDementia, mild cognitive impairment (MCI) and subjective cognitive decline (SCD) are referred to a continuum of cognitive impairment. But it is difficult to distinguished in clinical practice. Recently, the Korean version of Story Memory (SM) in the Korean‐Mini Mental State Examination, 2nd Edition: Expanded Version (K‐MMSE‐2: EV) was released to use. Thus, we aimed to find the correlation between cognitive status and score of SM test.MethodTwenty‐three patients with SCD, MCI, or mild dementia between ages of 60 and 80 were participated. They underwent K‐MMSE‐2, and Seoul Neuropsychological Screening Battery‐Second Edition (SNSB‐II). The correlation between score of standard version K‐MMSE‐2(T‐score) and SM score were analyzed. To identify the related functional brain area, analysis was performed with each domain in SNSB‐2 and SM scores.ResultSM score was associated with scores of standard version of K‐MMSE‐2(p = 0.013, Pearson correlation = 0.511). Also, the SM score have significant correlation with memory domain score of SNSB‐2(p = 0.048, Pearson correlation = 0.447).ConclusionWe found that patients' cognitive function and SM score have a positive relationship. It might be used to identify patients who might have progressed to Dementia or are about to progress. Also, MCI patients can be identified in early.
Exploring acceptance of monitoring technology in home‐based dementia care: A survey study among informal carers
Authors: Wrede Christian , Braakman‐Jansen Annemarie , van Gemert‐Pijnen Lisette 
Published: December 2023

AbstractBackgroundThere is growing interest to support home‐based dementia care via contactless monitoring technologies which do not require any body contact and allow informal carers to remotely monitor health and safety of people with dementia (PwD). However, sustainable implementation of such technologies requires a better understanding of informal carers’ acceptance. This study therefore aimed to explore 1) acceptance towards different use scenarios, 2) acceptance towards different contactless sensor types, and 3) differences between accepters and refusers of contactless monitoring technology for home‐based dementia care.MethodA cross‐sectional online survey was conducted among n = 304 informal carers of community‐dwelling PwD (Mage = 58.5±10.7; 71% female) in the Netherlands and Germany. Questions targeted informal carers’ general acceptance towards contactless monitoring technology, their acceptance towards 5 different use scenarios, their acceptance towards 7 different contactless sensor types, as well as their own and their care recipient’s personal characteristics. Data were examined using descriptive and bivariate analyses.ResultAcceptance towards different use scenarios for contactless monitoring technology varied significantly (p<.001). The intention to use contactless monitoring technology was highest for detecting emergency situations (e.g., falls, wandering), and lowest for predicting acute situations (e.g., fall prediction). Acceptance towards different contactless sensor types varied significantly as well (p<.001). RF‐based sensors (e.g radar) and light sensors were considered most acceptable, whereas camera‐based sensors and audio sensors were seen as least acceptable for home‐based dementia care. Lastly, accepters and refusers of contactless monitoring technology significantly differed regarding gender (p = .010), their relation with the PwD (p = .003), eHealth literacy (p = .025), personal innovativeness (p<.001), experience with safety technology (GPS trackers, alarm buttons) (p = .002), and the PwD’s type of cognitive impairment (p = .035) and housing situation (p = .023).ConclusionOur findings can help to make better‐informed decisions during development and implementation of contactless monitoring technology to support home‐based dementia care, thereby increasing the chance of achieving a fit between technology, user, and use context.
Recruitment of caregivers of persons living with dementia for an Online Support Group Programme– Learnings from India
Authors: Vasanthra Cicil R , Thirumoorthy A , Antony Sojan , Muliyala Krishna Prasad , Rao Girish N , N Anu K , Sivakumar Palanimuthu Thangaraju 
Published: December 2023

AbstractBackgroundCaregivers of persons living with dementia (CGPwD) tend to have limited access to dementia care‐oriented support group and relevant resources. The disparity in the ratio of mental health professionals available and the number of people with dementia plays a pivotal role in care and services. Support group interventions may not be available or accessible to the vast majority, more so in the Indian context. Hence, the need to test feasibility of providingsupport via using video‐conferencing software were explored.MethodFamily caregivers of those persons living with dementia who had accessed an out‐patient facility at a Geriatric Clinic as part of a tertiary care center between January 2020 – January 2023 was screened for recruitment into a single group quasi‐experimental study (n = 341). The responses of the family caregivers were noted down along with the experience of the mental health professionals involved in the process. Content analysis (Krippendorff, 2004, Neuendorf, 2002) was carried out using Atlas.ti.9 web version.ResultThe mean age of the CGPwD was 42.5 ± 8.5 years and the mean years spent in care‐giving was 4.02 ± 1.5 years; 42.8% of the persons living with dementia had a definitive diagnosis of Alzheimer’s Disease and majority were in the moderate stage of illness. One‐fourth of the caregivers showed interest to sign up for the support group with the first invitation. Of them, Spousal caregivers were noted to show more interest; followed by daughters‐in‐law. Apart from technological challenges, a good number of caregivers measured d that their loved one with dementia has “improved” due to decline in challenging behaviours.ConclusionUse of technology in psychosocial interventions is a relatively new concept in LAMIC countries such as India. Therefore, familiarization and orientation of such services may pave way to increase in demand for video‐conferencing based support groups at a larger level in the near future.
Recruitment data of the HAPPCAP‐AD (Human‐APPlication Combined Approach for Prevention of Alzheimer’s Disease): a feasibility study
Authors: Ganmore Ithamar , Ravona‐Springer Ramit , Livny Abigail , Niv Tal , Matatov Adar , Yore Iscka , Bem‐Moshe Ariela , Heymann Anthony , Beeri Michal S 
Published: December 2023

AbstractBackgroundPhysical inactivity, obesity, type 2 diabetes [T2D], hypertension, smoking, and depression, are six major modifiable risk factors for Alzheimer’s disease (AD). While several multi‐domain intervention studies have been completed, mostly in older adults, targeting one or more modifiable risk factor, results have been inconsistent. We designed novel, “real‐life”, feasibility study of a personalized 18‐months intervention for prevention of cognitive decline in middle‐aged individuals at very high AD risk due the presence of at least two modifiable risk factors plus parental family history of AD. Our aims are to explore: 1) The feasibility of our novel approach; 2) Whether our intervention improves AD risk factors and whether greater adherence is associated with larger improvement; and 3) Whether our intervention improves cognition.MethodStudy’s participants are 40‐65 years old cognitively normal offspring of individuals with AD. Participants must have at least two of the six major modifiable AD risk factors. Recruitment goal was 100 participants. Participants were randomized at a 2:1 ratio to the intervention:control arms of the study. The 18‐months intervention combines a smartphone application (app) with study team guidance through phone‐calls, which include advice for risk factors improvement, interactive follow‐up using an app, and feedback on risk factors management for its modification.ResultSince study was launch in 2019, 500 individuals have completed an online eligibility questionnaire. We have achieved the recruitment goal, and 100 participants have been enrolled, with 67 in the intervention group and 33 in the control group. Groups are well‐matched for baseline demographics and AD risk factors (intervention vs control: age‐ 57.3[SE = 0.64]vs.57.0[SE = 0.99]; sex ‐ [% males]30%vs.33%; years of education‐ 17.7[SE = 0.77] vs.17.0[SE = 0.64]; BMI‐ 30.5[SE = 0.63]vs.29.6[SE = 0.59]; % physical inactivity ‐84vs.82; % T2D‐ 15vs.12; % hypertension‐ 27vs.30; % depression‐ 16vs.15; and MMSE‐ 29[SE = 0.17]vs. 29.2[SE = 0.16]; all p>0.05), except for percentage of smokers which was significantly higher in the intervention arm (12vs.3). Additional analyses will be ready by AAIC 2023.ConclusionResults of this study will provide feasibility data on the adherence to the human‐app approach, characteristics of those who adhere, on whether the approach improves risk factors, and will provide initial data on intervention efficacy for cognition.