bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of a triage algorithm for mild cognitive impairment using electronic health record data
Authors: Li Gang , Batrla Richard , Betts Keith A. , DeSanti Susan , Frech Feride H , Galvin James E , Halpern Rachel , Juday Timothy , Toschi Nicola , Vandercappellen Jo , Mattke Soeren , Hampel Harald 
Published: December 2023

AbstractBackgroundTimely detection and diagnosis of early‐stage Alzheimer’s disease (AD) become increasingly relevant with the introduction of new therapies. Electronic health records (EHR) data present an opportunity to identify cases with elevated risk of undiagnosed cognitive impairment and triage them for further evaluation. Such triage tools are critically important because population‐based screening of asymptomatic individuals is currently not recommended, but spontaneous detection rates are low. We previously developed a risk prediction algorithm to identify likely cases aged ≥50 years based on claims data.MethodWe will build on this work and develop an algorithm based on three data sources: claims, EHR data and the National Alzheimer’s Coordinating Centers (NACC) data. The claims database study, already completed, included newly‐diagnosed mild cognitive impairment (MCI) individuals and non‐MCI individual matched on age, gender and geographic region. Using data 2 years prior to diagnosis, medical conditions associated with MCI were used to build a predictive model. The EHR study will aim to augment the model with clinical data such as body mass index, smoking status, vital signs and laboratory results. The NACC study, including ∼5,400 MCI and ∼15,800 cognitively normal (CN) individuals, will be used to validate the algorithm.ResultThe claims database study identified 25 medical conditions that predict MCI risk (area under the curve, AUC = 0.78). The top five predictors for MCI were depression, stroke/transient ischemic attack, obstructive sleep apnea, hyperlipidemia, and hypertension. The predictive performance was better in the 50‐64 year than in the older age groups. Analyses of the EHR and NACC data are ongoing.ConclusionData routinely collected in claims and EHR may be accurate enough to triage individuals with potential MCI for cognitive assessment in primary care.
Deep Learning for EEG‐Based Alzheimer’s Disease Diagnosis
Authors: Zhang Xiang , Wang Yihe , Chandak Payal , He Zihuai 
Published: December 2023

AbstractBackgroundElectroencephalography (EEG) could be a powerful tool to diagnose Alzheimer’s Disease (AD) because it is pervasive, non‐invasive, and cost‐effective [1]. However, EEG‐based AD detection has suffered from poor performance and low generalizability due to the noisy nature of the signal and high inter‐patient variability. Here, we show that emerging deep learning techniques can alleviate these challenges and pave the way for the clinical use of EEG in AD detection.MethodWe formulate the problem of EEG‐based AD detection as a binary classification task. We propose an end‐to‐end deep neural network that utilizes contrastive representation learning to automatically learn low‐dimensional features from EEG trials. These features are then fed into a non‐linear classifier that predicts the probability of AD.ResultWe evaluated the model on a public dataset comprising 23 subjects (12 AD; 11 control) that have 663 trials in total [2]. (1) Patient‐dependent setup. In line with existing work in EEG‐based AD detection, we randomly assigned 80% of trials for training and the remaining 20% for tests, where the same patient could have trials in both groups. Our model achieved an F1 score of 99.35% which is competitive with, if not superior to, the state‐of‐the‐art baselines. (2) Patient‐independent setup. We then investigate a more challenging setup, where trials from a particular patient were assigned either to training (19 subjects) or to testing (4 subjects). The deep learning system is evaluated on patients it has not encountered before. Our model attains an F1 score of 86.45%, outperforming baselines by a significant margin (>20%).ConclusionOur results demonstrate that our deep learning model significantly improves the accuracy of EEG‐based AD diagnosis. In particular, our model sets the state‐of‐the‐art performance in the patient‐independent evaluation. This suggests that our model can learn a distinctive pattern of AD from a small group of subjects and apply it to unseen individuals: which is indispensable for real‐world deployment.[1] Tait, X. et al. Eeg Microstate Complexity for Aiding Early Diagnosis of Alzheimer’s Disease. Scientific Reports, 2020.[2] K. Smith, et al. Accounting for the Complex Hierarchical Topology of Eeg Phase‐Based Functional Connectivity in Network Binarisation. PloS One, 2017.
Examining the Interactive Effect of Cardiovascular Risk Factors and APOE Genotype on Population Health Disparities in dementia risk
Authors: Andrews Shea J , Renton Alan E. , Fulton‐Howard Brian , Brenowitz Willa D , Van Hulle Carol A. , Li Clara , Yaffe Kristine 
Published: December 2023

AbstractBackgroundThere is a need to better understand the role of modifiable risk factors in the development of racial/ethnic health disparities in Alzheimer’s disease (AD). In particular, research is lacking on how clinical risk scores may interact with genetic liability for dementia in diverse populations. Here, we examined the influence of the Cardiovascular Risk Factors, Aging, and Incidence of Dementia Risk Score (CAIDE) and APOE genotype on MCI/Dementia risk in non‐Latinx White (NLW), Black, and Latinx populations.MethodParticipants were from the National Alzheimer’s Coordinating Center Uniform Dataset who self‐reported as NLW (n = 15,296), Black (n = 2,136), or Latinx (n = 955) who were either cognitively normal or had a clinical diagnosis of MCI/Dementia at baseline (Table 1). CAIDE is comprised of age, gender, years of education, hypertension, obesity, and hypercholesteremia with scores ranging from 0‐14. Logistic regression models stratified by race/ethnicity were used to examine the association between CAIDE, APOE genotype, or their combination (9 categories combining favorable, intermediate, and unfavorable CAIDE scores with APOE e3/e3, e2+, and e4+) on MCI/dementia (Fig. 1‐2). Population differences were determined using Fisher’s z‐score method.ResultsAPOE e2+ was associated with reduced odds of MCI/dementia in NLW participants, while APOE e4+ was associated with increased risk in Latinx, Black, and NLW participants. Higher CAIDE scores were associated with increased risk of MCI/dementia in Latinx and NLW but were non‐significant in Black participants. The association of CAIDE on MCI/dementia in Black participants was significantly different from that in NLW and Latinx participants. Similarly, the effect of APOE e4+ on the risk of MCI/dementia was weaker in Latinx compared to NLW participants. In NLW, increasingly unfavorable modifiable risk profiles attenuated the protective effect of APOE e2+ and accentuated the deleterious impact of APOE e4+ on MCI/dementia. In Black participants, modifiable risk factors did not significantly affect the relationship between APOE genotype and MCI/dementia; in Latinx participants, there was only a moderate effect.ConclusionThe risk associated with APOE on MCI/Dementia is moderated by modifiable risk factors with suggestive population‐specific effects. Addressing health disparities will require risk scores that combine clinical, genetic, and social determinants of health and are applicable across diverse populations.
Impact of Multivitamin‐Mineral and Cocoa Extract on Incidence of Mild Cognitive Impairment and Dementia: Results from the Cocoa Supplement and Multivitamin Outcomes Study for the Mind COSMOS‐Mind
Authors: Rapp Stephen R. , Sachs Bonnie C. , Williams Benjamin J. , Gaussoin Sarah A. , Baker Laura D , Manson JoAnn E. , Sesso Howard D. , Shumaker Sally A. , Espeland Mark A. ,   COSMOS‐Mind Research Group
Published: December 2023

AbstractBackgroundCOSMOS‐Mind, a large, randomized placebo‐controlled pragmatic trial in older adults, recently reported that daily supplementation with multivitamin‐mineral, but not cocoa extract, benefited global cognition. We assessed effects of these supplements on prespecified secondary outcomes of mild cognitive impairment (MCI) and all‐cause dementia (DM). We also assessed treatment‐related cognitive trajectories leading to conversion to MCI or dementia.MethodCOSMOS‐Mind (N = 2262) administered a telephone‐based cognitive battery at baseline and annually for 3 years. Proxies of participants meeting criteria for suspected cognitive impairment based on a cognitive screener (TICSm) were interviewed. An expert panel adjudicated cognitive status. Incidence rates of MCI, and separately DM, were compared among arms with proportional hazards regression. Changes in cognitive trajectories leading to MCI and DM were assessed with linear models.ResultAt baseline, adjudication for suspected cognitive impairment was triggered for 419 (20%) participants, yielding adjudications of 217 (10%) MCI and 4 (0.2%) DM cases. During follow‐up, triggers were less frequent in the MVM than placebo arms: hazard ratio = 0.73 (p = 0.04). Across 3 years, 110 incident MCI and 14 incident DM cases were adjudicated. Incidence rates of these outcomes did not vary by assignment to multivitamin‐mineral or cocoa extract (all p≥0.05), however statistical power was low. Incident MCI was associated with a marked decline in cognitive function from the previous year (p<0.001). When participants assigned to multivitamin‐mineral versus placebo converted to MCI, their scores for global cognition (p = 0.03) and executive function (p<0.001) were higher and had declined less relative to the previous year (p = 0.03 for global cognition; p = 0.004 for executive function). Daily cocoa extract use did not attenuate cognitive decline leading up to MCI.ConclusionCOSMOS‐Mind does not provide evidence that cocoa extract or multivitamin‐mineral therapy lowers risk of incident MCI or DM. Multivitamin‐mineral therapy, but not cocoa extract, may provide cognitive resilience countering conversion to MCI.
Age in Place, Move in VR: Investigating the Feasibility and Usability of a Custom‐made Virtual Reality Exergame to Promote the Well‐being of Community‐Dwelling Older Adults
Authors: Mehrabi Samira , Basharat Aysha , Mazen Sarah , Cao Shi , Boger Jennifer , Barnett‐Cowan Michael , Muñoz John , Middleton Laura E 
Published: December 2023

AbstractBackgroundInteractive virtual reality (VR) games combined with exercise (exergames) are a plausible strategy to encourage physical activity (PA) among older adults. However, there is little systematic evaluation of the feasibility, usability, and potential benefits of deploying at‐home VR exergames among this population.MethodFifteen community‐dwelling older adults (M = 67.33 ± 5.3, age range 60‐77) (Table1) were recruited to play 18 sessions (each 15‐20 min) of a custom‐made VR exergame at home over six weeks. Recruitment, retention, and adherence rates were calculated to determine feasibility. The usability of the exergame was assessed through i) a game user experience questionnaire, ii) a self‐reported physical/emotional discomfort questionnaire, and iii) a perceived enjoyment scale. The exploratory outcome measures included changes in i) PA, ii) exercise self‐efficacy, iii) affect, iv) various cognitive and perceptual tasks and were assessed i) before and after acute data, and ii) before and after the intervention.ResultThirteen participants completed the study (13% attrition rate) without missing any exergaming session (100% adherence rate). The completion rate of cognitive assessments, perceptual tasks, and self‐reported questionnaires was ≥ 97%. Low levels of cybersickness were reported (M = 0.9 ±1.0 out of 10). The average perceived enjoyment and perceived rate of exertion were 3.2. ±1.1 (out of 5) and 2.4±1.9 (out of 10), respectively. Most of the participants (92.3%) found the exergame’s instructions easy to follow and had an overall positive experience (84.6%). Whereas the exergame was perceived as useful for increasing PA levels (61.6%), mood enhancement was reported among less than half of participants (46.2%) and only 38% indicated that they would choose VR exergaming for at‐home PA. The likelihood of playing VR exergames in the future was also low (38.5%). The exploratory outcome measures are presented in Table 2.ConclusionOur study provides insights into the feasibility and usability of a custom‐made VR exergaming intervention to promote PA in community‐dwelling older adults which may potentially benefit their well‐being. Our findings inform the future adoption of VR at home. Our results can also be used to inform large‐scale trials and support the design and deployment of other remote exergames and assessments.
Patient‐Public Involvement (PPI) in Dementia Trials Ireland (DTI): A network to increase capacity and capability to deliver clinical trials for dementia in Ireland
Authors: Leroi Iracema , Miller Anne‐Marie 
Published: December 2023

AbstractBackgroundDementia Trials Ireland (DTI) is a newly developed national network to increase capacity and capability to deliver clinical trials for dementia in Ireland. DTI activities are informed by a panel of people with lived experience of dementia and their care partners, following principles of 'patient and public involvement and engagement (PPIE).MethodPeople with lived experience of dementia joined the DTI panel to provide consultation on the design and conduct of industry‐sponsored and investigator‐led clinical trials, providing insight into the lived experience of dementia. Monthly meetings and consultation workshops enable panel members to provide feedback to the DTI trial teams.ResultThirteen PPI panel members represented lived and care partner experience of FTD, AD, LBD, VaD have informed the following DTI network activities: (1) Advising on appropriate language use for trial Participant Information Sheets; (2) Co‐developing the logo for DTI; (3) Advising on our website and video content; (4) Representing DTI at online conferences and workshops; and (5) Providing researchers and clinicians with feedback on the lay summaries of scientific papers.ConclusionThe panel has made meaningful changes to various outputs and activities of the trials' network, ensuring that the perspectives of people with lived or caregiver experience of dementia are included.
Resource utilization for pre‐screening study patients with cognitive disorders.
Authors: Gervais Pierre 
Published: December 2023

AbstractBackgroundDrug development is a complex, expensive, lengthy, and multifaceted process. Patient recruitment tends to be a considerable and underestimated variable when it comes to planning and implementing a study. More information about evaluating patient sources and resource allocation will allow sites and sponsors to use their resources more effectively and to complete clinical projects earlier.MethodThis paper reviews the sourcing of patients and resource utilisation in hours for 6 pivotal Cognitive Disorder studies by gathering all patients contacted in various phases of a project: pre‐screening, screening, and randomization.ResultSourcing of patients was tracked for pre‐screening, screening and randomisation and are compared in number of calls, resource utilisation (hours), and number of randomized patientsA total of 1 726 patients were pre‐screened, which represents 575 hours of resources, the largest proportion of pre‐screened calls, 43%, was generated from internal database of patients resulting in 46% of randomized patients.The second largest proportion of pre‐screened patients originated from newspapers with 18% of contacts that required 13% of resources and resulted in 14% of randomized patients.Additional sources of pre‐screened calls were conferences (8%), canvassing (7%), friends (4%), medical references (3%), memory day (9%), radio (2%) and web site (2%).ConclusionWe report a pre‐screened/screened/randomized ratio of 23:5:1 indicating that pre‐screening is important to secure patient enrolment though largely underestimated and rarely if ever accounted for in budget assessments. Given the current decline in the use of paper media, alternate solutions must be developed to replace this otherwise successful communication tool. More information is required to better characterize each recruitment method and to determine to which extent the randomization ratio is favorable with each initial source of patients.
Lived‐experienced of people living with dementia in Mauritius
Authors: Dorkhy Geeta Devi 
Published: December 2023

AbstractBackgroundMauritius has an ageing population of about 20%. There is a rise in the number of dementia due to increased prevalence of risk factors in the population. The interest of the researcher is to explore the daily living experience of people with dementia. Therefore, the understanding of the lived‐experience as detailed by the person himself/ herself is a unique, highly valued and rich adventure.MethodIn order to capture the lived‐experience of people with dementia, an audio‐recorded smartphone was used. One‐to‐one interview was conducted for 30‐40 minutes. Short interview questions relating to the daily living activities was used. Raw data was obtained until theoretical saturation. Data was decoded and grouped into sub‐themes, themes and main themes using thematic analysis.ResultLived‐ experience of person with dementia (n = 20) is rich. New data emerges from the interview process, exploring new dimensions of unknown. Seven of the main themes were ‘dependency’, ’family support’, ‘memory recall’, ‘caring’, ‘music and singing’, ‘activity’ and ‘knowledge on Alzheimer’s disease’. High emotions and the facial expressions were equally important.Conclusion‘Quality is more important than quantity’ in examination of real‐ life experiences.
Proteomics identifies immunological drivers of post‐infection brain atrophy and cognitive decline
Authors: Duggan Michael R , Peng Zhongsheng , Sipilä Pyry , Lindbohm Joni V , Kivimaki Mika , Davatzikos Christos , Erus Guray , Candia Julian , Daya Gulzar , An Yang , Karikkineth Ajoy , Lewis Alexandria , Moghekar Abhay , Walker Keenan A. 
Published: December 2023

AbstractBackgroundPrior infections can modify the risk for dementia and neurodegenerative disease, yet the mechanisms by which infections contribute to neurodegeneration remain unclear.MethodWe examined how infection diagnoses documented in the Baltimore Longitudinal Study of Aging (BLSA) are associated with rates of change in brain volumes over time, and how these same infections relate to dementia risk using hospital records from UK Biobank and a Finnish multicohort sample (the Finnish Public Sector study, the Health and Social Support study, and the Still Working study). After determining how infections differentially relate to an immunological plasma proteome (942 proteins) in BLSA participants, we then identified a subset of infection‐related proteins that also predict changes in brain regions vulnerable to infection‐specific atrophy (candidate proteins). In addition to evaluating their associations with longitudinal cognitive performance as well as Alzheimer’s disease and related dementias (ADRD) plasma biomarkers (Aβ42/40, GFAP, NfL, pTau‐181), we also investigated the causal link between candidate proteins and longitudinal brain volume loss using Mendelian randomization. Study design is illustrated in Figure 1.ResultA total of 982 cognitively normal participants (age: 65.4 [SD 14.9]); 55.2% female; 66.9% white) were included in the primary MRI analysis. Of the 15 infections examined, we identified six diagnoses related to accelerated brain volume loss, including influenza, viral, respiratory, and skin/subcutaneous infections, with atrophy commonly observed in the temporal lobe. Each infection linked to brain volume loss in the BLSA was associated with increased risk of dementia in the UK Biobank (n = 495,896), with many also associated with increased risk in the Finnish multicohort sample (n = 273,132). From a set of 260 infection‐related proteins, we identified 35 candidate proteins that also predicted changes in brain regions vulnerable to infection‐specific atrophy (Figure 2A‐F). Many candidate proteins were related to cognitive decline, plasma ADRD biomarkers or mechanistically implicated in longitudinal brain volume loss in Mendelian randomization analyses (Figure 2G‐H; Figure 3A).ConclusionWe demonstrate that infections are linked to accelerated regional brain atrophy and dementia risk. Additionally, we identify molecular mediators by which infections may contribute to neurodegeneration (Figure 3B).
The technological reserve hypothesis: A meta‐analysis
Authors: Benge Jared , Scullin Michael K 
Published: December 2023

AbstractBackgroundOlder adults are increasingly crossing the digital divide, regularly engaging with technologies such as the internet, smartphones, and personal computers. Two distinct theories of the cognitive impact of this digital engagement have been raised. According to the digital distraction hypothesis, regular engagement with technology acutely negatively effects cognition by increasing distractions and decreases cognitive skills over the long‐term by regularly offloading tasks such as recall of facts, directions, and other daily memory tasks into digital technologies. By contrast, according to the technological reserve hypothesis, regular engagement with technology acutely benefits functioning via use of assistive technologies and can reduce long‐term risk for cognitive disorders with age by promoting complex cognitive engagement, sustained connection, and acquisition of digital based compensatory skills.MethodRandom effects meta‐analysis was used to pool effect sizes, with primary effect sizes reported as odds ratios (odds of developing a cognitive diagnosis or odds of having lower cognitive scores as appropriate to each study). We conducted systematic evaluations of study quality and to determine the influence of socioeconomic, education, age, and type of technology.ResultOf 6,183 records identified, 26 studies met inclusion criteria and were included in the final analysis. Engagement with smartphones, computers, and/or the internet was associated with decreased risk of cognitive decline, mild cognitive impairment, and dementia (pooled OR = 0.45, 95% CI 0.318‐0.637, p<.0001; I2 = 99.2%; Figure 1). Potential confounding variables, such as adjustment for socioeconomic factors and education or type of technology measured did not moderate this relationship (Figure 2). Study quality was generally good (Figure 3), though all studies assessed technology use by self‐report and few measured intraindividual changes in technology use over time.ConclusionEngagement with everyday digital technologies is associated with decreased risk of cognitive decline and dementia, consistent with the technological reserve hypothesis. While these studies do not prove causality of this relationship, they do highlight the potential for digital health approaches to promote cognitive health and well being with age. Limitations, particularly with regard to understanding digital disparities and incorporating objective digital use metrics in future studies are discussed in detail.