bluuebunny/crossref_metadata_2025_split huggingface dataset:
Algorithmic Fairness in Biomarker‐Based Machine Learning Models to Predict Alzheimer’s Dementia in Individuals with Mild Cognitive Impairment
Authors: Sahin Derya , Jessen Frank , Kambeitz Joseph ,   Alzheimer's Disease Neuroimaging Initiative (ADNI)
Published: December 2022

AbstractBackgroundWith increasing availability of large‐scale data sets in biomedical research, machine learning (ML) algorithms rapidly gain importance for research and clinical practice. However, ML models bear the risk of increasing health disparities, whenever trained with imbalanced or biased data. Based on these ethical concerns the WHO guidelines on Artificial Intelligence for Healthcare recommend incorporating algorithmic fairness in ML models by design. Although research on biomarker detection and dementia risk prediction in Alzheimer’s disease increasingly employs ML algorithms, the fairness of these models has not been in focus to date.MethodUsing data from the Alzheimer’s Disease Neuroimaging Initiative database, we generated ML models to predict conversion to Alzheimer’s dementia in 1017 individuals with mild cognitive impairment. Two models were trained based on (1) biomarkers and (2) biomarkers and demographic features. Subsequently, fairness metrics were calculated to investigate prediction disparities in the trained models regarding sensitive attributes sex, education, race and ethnicity.ResultThe models did not show substantial differences in predictive performance. Compared to Non‐Hispanic‐White individuals, true positive rates for Hispanics and for Non‐White individuals were lower in the biomarker model. Inclusion of demographic features to the model led to a further drop of true positive rates and positive predictive values for both Hispanics and Non‐White individuals. None of the models showed any predictive disparities regarding sex and education, with both models having a slightly higher accuracy, positive predictive value, and negative predictive value for women and for individuals with lower educational attainment.ConclusionThis study empirically demonstrates the potential ethical hazards of ML models for the prediction of conversion to Alzheimer’s dementia. While ML models exhibit fair prognostication when based on balanced data regarding sensitive attributes, underrepresentation of specific groups may lead to violations of fairness criteria with important implications for the clinical translation of these models. Such fairness disparities can be present in models based on only biological data and the inclusion of demographic information might exacerbate the predictive disparities in ML‐models.
Computerized cognitive interventions for preventing dementia in people with mild cognitive impairment: A systematic review with meta‐analyses
Authors: Zuschnegg Julia , Schoberer Daniela , Häussl Alfred , Russegger Silvia , Ploder Karin , Fellner Maria , Hofmarcher‐Holzhacker Maria , Roller‐Wirnsberger Regina , Paletta Lucas , Koini Marisa , Schüssler Sandra 
Published: December 2022

AbstractBackgroundPeople with mild cognitive impairment (MCI) are faced with a higher risk of developing dementia, but with a "window of opportunity" for prevention and treatment. Computerized cognitive interventions (CCIs) might constitute a beneficial non‐pharmacological treatment approach. This systematic review evaluated evidence of CCIs performed individually with a computer, tablet‐PC, game consoles, virtual reality (VR), or augmented reality (AR) on cognition in community‐dwelling people with MCI.MethodA systematic review and meta‐analyses of randomized controlled trials (RCTs) was performed. The systematic literature search was conducted in MEDLINE, CINAHL, Embase, Cochrane CENTRAL, IEEE Xplore Digital Library, ISI Web of Knowledge, Scopus and PsycINFO, and included an additional search for gray literature. The search was completed by backward citation tracking. The methodological quality of the included RCTs was independently assessed by two reviewers. Comparable studies were pooled using the random‐effects model.ResultA total of 18 randomized controlled trials (RCTs) were identified. 12 RTCs were used for conducting meta‐analyses which showed significant effects of CCIs for people with MCI in memory (SMD 1.12, CI 95 % [0.08, 2.16]), working memory (SMD 0.85, CI 95 % [0.32, 1.38]), attention/concentration/processing speed (SMD 0.61, CI 95 % [0.21, 1.02]), executive functioning (SMD 0.96, CI 95 % [0.26, 1.66]), and language (SMD 1.63, CI 95 % [0.20, 3.06]), but no significant improvement in global cognition. Pooling follow‐up evaluations was not possible, but one of two studies showed continuing significant differences for the intervention group after 6 months post intervention in memory and working memory. CCIs were conducted predominantly with computers, followed by tablet‐PCs, VR, AR, and mixed reality.ConclusionThis review demonstrated beneficial effects of CCIs on domain‐specific cognition in people with MCI. Therefore, CCIs might be a valuable treatment option to maintain cognition and prevent progression to dementia. However, further research is necessary, especially on CCIs applied with emerging technologies like VR or AR. Furthermore, various outcome measurements used in the studies were challenging for the pooling of study results, which highlights the need for a predefined set of important cognitive outcomes and tools for future RCTs.
Weighted Gene Co‐Expression Network Analysis for peripheral whole‐blood RNA sequencing in asymptomatic Alzheimer’s disease
Authors: Luckett Emma S. , Zielonka Magdalena , Schaeverbeke Jolien , Adamczuk Katarzyna , Van Laere Koen , Dupont Patrick , Cleynen Isabelle , Vandenberghe Rik 
Published: December 2022

AbstractBackgroundAlzheimer’s disease (AD) blood‐based biomarkers are becoming widely researched. Their requirement in the asymptomatic phase of AD is high to determine individuals who are in the early stages of disease. We investigate gene network expression to better understand molecular changes in this asymptomatic phase.MethodRNA was extracted and sequenced for 63 cognitively healthy Flemish Prevent AD Cohort KU Leuven (F‐PACK) participants (70 (56‐80) years) at baseline and follow‐up (interval: 4.6 (3.4‐8.59) years, 30 APOE4 carriers). Participants received amyloid‐PET and structural MRI at both timepoints. We performed weighted gene co‐expression network analysis (WGCNA) to identify networks of co‐expressed genes at baseline. These modules were correlated with traits of interest (APOE4 (present/absent) and amyloid rate of change ([follow‐up – baseline amyloid load], divided by time interval (years)) using Pearson’s correlation coefficient. Significantly correlated modules were functionally annotated using Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) in R. Potential hub genes were identified if the module contained genes with eigen‐based connectivity >0.7. These were visualised using STRING to create protein‐protein interaction networks. The top four hub genes (with highest Maximal Clique Centrality values) were then selected.ResultA gene co‐expression network was built on 9,149 genes that passed the read count threshold (genes with >10 read counts in >90% of samples). From this co‐expression network, 22 co‐expression modules were constructed (Figure 1A) and correlated with traits of interest. For the tan module (including 188 genes) APOE4 carriership was significantly negatively correlated (p=0.03, r=‐0.22), whereas amyloid rate of change was significantly positively correlated (p=0.03, r=0.21). Top GO processes for this module included T cell activation and differentiation, and top KEGG pathways included T cell receptor signalling, and Th1 and Th2 cell differentiation. The top four hub genes were GZMA, NKG7, TBX21 and PRF1 (Figure 1B).ConclusionBaseline gene expression networks differ between APOE4 carrier status, and with respect to amyloid rate of change. This suggests there are network alterations in the asymptomatic phase of AD that can be detected in blood. These results may aid in development of targets for biomarker development or druggable targets for AD.
Using a digital assessment of multi‐day learning curves to detect preclinical AD
Authors: Weizenbaum Emma L , Soberanes Daniel , Hsieh Stephanie , Schneider Olivia R , Buckley Rachel F. , Properzi Michael J , Rentz Dorene M. , Johnson Keith A. , Sperling Reisa A. , Papp Kathryn V. , Amariglio Rebecca E. 
Published: December 2022

AbstractBackgroundUnsupervised remote digital cognitive assessment makes frequent testing feasible and allows for measurement of learning across days on participants’ own devices. More rapid detection of diminished learning may provide a potentially valuable metric for clinical trials seeking to capture change over shorter intervals. Here, we assess whether clinically normal (CN) participants differ in their learning of the same stimuli across daily exposures depending on their β‐amyloid (Aβ) burden.MethodThe Boston Remote Assessment for Neurocognitive Health (BRANCH) (Papp et al., 2021) is a web‐based assessment administered over 7 consecutive days repeating the same stimuli each day to capture multi‐day‐learning‐curves (MDLC). The composite score includes accuracy on three cross‐modal associative memory tasks: face‐name, groceries‐prices, digits‐signs. Our sample consisted of 192 CN older adults enrolled in ongoing observational studies (mean age = 74.0, 63% female, 87% Caucasian, mean education = 16.6) who completed the tasks daily at home on their own digital devices. Participants had previously completed in‐clinic paper‐and‐pencil tests to compute a Preclinical Alzheimer’s Cognitive Composite (PACC‐5) as well as PET imaging with 11C‐Pittsburg Compound‐B to estimate globalAβ burden and classify participants as Aβ+ and Aβ‐. Mixed‐effects models controlling for age, sex, and education were used to observe the associations between Aβ status and BRANCH‐MDLC; and AUC analyses compared classification of amyloid using BRANCH‐MDLC vs. PACC‐5.ResultAdherence was high with 95% of participants completing all seven days of consecutive assessment. Aβ+ status (n = 26) was associated with a lower BRANCH‐MDLC across 7‐days (t = ‐4.03, p<.001) compared with Aβ‐ status (n = 148). Amyloid status was not associated with difference in Day 1 BRANCH or PACC‐5 performance. Furthermore, BRANCH‐MDLC showed a significant ability to classify individuals as Aβ+ vs. Aβ‐ (AUC = 0.71, p = <.001) compared to a single‐timepoint pencil‐and‐paper composite (PACC‐5) (AUC = 0.52, p = 0.56).ConclusionDiminished task learning across seven days was associated with greater amyloid pathology, whereas Day 1 of BRANCH or an in‐person supervised one‐time neuropsychological assessment were not associated with elevated amyloid. These findings signal the value of measuring differences in learning across days as a means towards capturing biomarker‐associated memory decrements and identifying those in the preclinical stage of AD.
Neuroanatomy of dementia in Down Syndrome revealed using a voxel‐based morphometry approach to computed tomography
Authors: Zhang Linda , Sanchez Beatriz , Moldenhauer Fernando , de Asua Diego Real , Brudfors Mikael , Foulon Chris , del Ser Teodoro , Ashburner John , Nachev Parashkev , Strange Bryan A 
Published: December 2022

AbstractBackgroundPeople with Down syndrome (DS) are more likely to develop Alzheimer’s disease than the general population, but comorbidities and variable intellectual disability complicate diagnosis and disease monitoring. Although neuroimaging may help elucidate the pathogenesis of dementia, studies using magnetic resonance imaging (MRI) in DS subjects with dementia are limited due to greater motion artefact in those with severe cognitive impairment. Computed tomography (CT) is faster, thus less impacted by motion, but poor tissue contrast makes analysis of cortical and subcortical changes difficult. In this study, we use CTseg, an algorithm able to robustly spatially normalise and segment CT scans, to analyse grey matter (GM) and white matter (WM) differences in 98 DS subjects with and without cognitive impairment due to dementia.MethodsThe CT scans of 120 DS patients from the Adult Down Syndrome unit at the Hospital Universitario La Princesa were selected. Dementia level was classified by retrospective review of medical records as 0 (cognitively stable), 1 (mild dementia), 2 (moderate dementia), or 3 (advanced dementia) according to the DSM‐V operational criteria. Premorbid functionality was assessed using Part 1 (“Level of ‘best’ ability”) of the Dementia Screening Questionnaire for Individuals with Intellectual Disabilities (DSQIID). Those with artefacts, faulty scans, or incidental brain findings were excluded, leaving a final sample of 98 subjects (mean age: 48±8.5 years; 45% female; 62 cognitively stable). CT scans were segmented into GM, WM, and cerebrospinal fluid using CTseg (https://github.com/WCHN/CTseg). Voxel‐based morphometry (VBM) analyses were carried out separately on smoothed and modulated GM and WM images, correlating with dementia level whilst controlling for age, sex, DSQIID, and total intracranial volume.ResultsGM volume was significantly negatively correlated with dementia level in the bilateral parahippocampal gyri, extending into hippocampus, and amygdalae. By contrast, significant WM reductions associated with dementia level were primarily parietal and occipitotemporal and right‐lateralised in the superior and inferior longitudinal fasciculi (p<0.001, FWE‐corrected).ConclusionsOur study uses a novel algorithm that allows the analysis of CT scans using VBM methods, enabling the inclusion of a sizeable sample of DS subjects with dementia, a group difficult to study with neuroimaging due to practical constraints.
The cost burden of Alzheimer’s Disease on care partners
Authors: Hahn‐Pedersen Julie Hviid 
Published: December 2022

AbstractBackgroundAlzheimer’s Disease (AD) has significant impact on care partners. We conducted a systematic literature review to assess the economic impact of AD with special attention to care partners and disease progression.MethodA protocol was developed before searches in PubMed, Embase and PsycINFO were run November 2021 via the Silvi.ai‐platform. Conduct of the systematic literature review followed standard guidelines. Eligible studies were in English and provided data from earliest Jan 2011 – Nov 2021.ResultOf 1043 identified hits, 25 studies were relevant for data extraction. AD costs were defined heterogeneously between studies, however most studies included AD drugs, out‐ and inpatient visits as well as non‐AD drugs (anti‐psychotics, supplements etc).7 of the 25 studies specifically explored the cost burden of AD for care partners; hours needed to support the partner, loss of work productivity, and implications for future employability.Monthly cost of caretaking was similar across studies and countries and varied between €643 ‐ €1050 for mild AD in Germany and Spain, €804‐ €1329 for moderate AD in France and Germany, to €1515 ‐ €2376 for severe AD in UK and Germany, respectively. The major cost driver was severity of the clinical status of the patient.Only few studies reported on work impairment of care partners. A study comparing matched care partners vs. non‐care partner demonstrated significantly higher activity impairment (25.4% vs. 21.8%), greater absenteeism (5.4% vs.3.1%), presenteeism‐related impairment (22.7% vs. 18.4%), and overall work impairment (25.8% vs. 20.4%). Same pattern, showing an increase in work impairment with AD severity was seen across countries (US, Japan and EU5).ConclusionMost identified studies focused on the cost burden on society or patients. In 7 studies reporting cost impact on care partners, the cost burden was very similar across countries and health care systems. Cost impact on care partners increased from early to severe AD. In particular, work impairment increased significantly with AD severity. However, more research is needed to identify the true cost burden for care partners.
A Compendium Of Two Decades Of Economic Evaluations In Dementia And A Critique Of The Implied Use Of Resources
Authors: Trepel Dominic 
Published: December 2022

AbstractBackgroundThis paper presents a unified source of all health economic evaluations related to four categories of resource allocation in dementia (identification, pharmaceuticals, non‐pharmacological interventions and management strategies) and, for health professionals (i.e. non‐economists), we provides guided critiques of implied trade‐offs across varying allocation decisions.MethodsTo take stock of the entirety of economic evidence, we systematically review, and quality assess, all peer reviewed cost‐effectiveness analyses published since 1999. We provides in‐depth expert guidance, and objective critical appraisal, through the variety of implied inferences related to potential allocation decisions and avoid sources of underlying biased. We explain how to interpret, and differentiate between, the two common forms of economic evidence (i.e., trial‐based or model‐based cost effectiveness analysis). We illustrate strengths and weaknesses of our included studies and, towards informing optimal resource allocation, we detail the opportunities and threats in relation to quality of evidence.ResultsSince 1999, we found 127 published economic evaluations (identification, 20%; pharmaceuticals, 45%; non‐pharmacological approaches, 18% and; management strategies, 17%). Internationally, the top five countries producing economic evaluations in dementia were: UK (33%); USA (19%); the Netherlands (10%) Canada (7%) and; (jointly) Germany/Sweden (5%). Over time, production of economic evidence appears relatively flat (averaging 1 to 2 publications per category) but significant upsurges in pharmaceutical‐related studies occurred in 2005 (n = 5), 2010 (n = 8) and 2012 (n = 8). Reporting is generally found to meet objective international standards, however, our critique of model‐based techniques raises concerns. Examining funding sources, industry‐funded pharmaceutical studies appear more likely to report more favourable cost effectiveness results, with these exclusively simulation‐based studies often purporting more cost savings (as compared to publicly‐funded studies). Notwithstanding concerns, the sum of evidence indicates that, compared to usual dementia care, alternative resource allocation can often result in increased quality‐of‐life, whilst also reducing costs.ConclusionIn contrast to competing disease areas, dementia research has experienced a relatively low production of economic evidence. Given the considerable economic changes created by dementia, and potential for economic benefit highlight here, increasing the inclusion of high quality economic evaluations alongside dementia‐related research is required.
Elevated tau‐PET signal predicts near‐term conversion to mild cognitive impairment in cognitively unimpaired older adults
Authors: Strikwerda‐Brown Cherie , St‐Onge Frederic , Binette Alexa Pichet , Ozlen Hazal , Buckley Rachel F. , Sperling Reisa A. , Johnson Keith A. , Poirier Judes , Breitner John , Villeneuve Sylvia ,   HABS Research Group, PREVENT‐AD Research Group
Published: December 2022

AbstractBackgroundNational Institute on Aging‐Alzheimer’s Association workgroups have proposed biological research criteria to identify individuals with preclinical Alzheimer’s disease (AD). It remains unclear whether cognitively unimpaired individuals with abnormality in both amyloid (A) and tau (T) biomarkers will go on to develop a clinical AD syndrome. In two independent cohorts, we investigated whether elevated tau‐PET signal in persons with elevated amyloid‐PET predicts near‐term conversion to mild cognitive impairment (MCI). We also tested whether neurodegeneration (N), a non‐specific marker typical of later‐stage AD, improved such prediction.MethodWe studied 128 cognitively unimpaired participants from the parental history‐positive PREVENT‐AD cohort and 153 individuals from the Harvard Aging Brain Study. Participants had >1 year of clinical evaluation following tau‐PET scanning (PREVENT‐AD median follow‐up time = 3.21 years [1.51‐4.50]; HABS median=1.94 years [1.13‐5.42]). At the time of PET, participants were stratified as abnormal (+) or normal (‐) on global amyloid‐PET (A), a temporal tau‐PET meta‐ROI (T), and hippocampal volume (N). Tau‐PET scans were also visually inspected for significant neocortical binding.ResultWithin the A+T+ groups, 61.5% (8/13; PREVENT‐AD) and 45.5% (5/11; HABS) of participants converted to MCI during follow‐up, compared with <13% of participants in all other PET‐biomarker groups (Figures 1 & 2). In Cox regression models, hazard ratios for conversion to MCI in the A+T+ group vs. the other biomarker groups were all >5 (Figure 3). Importantly, the majority of the A+T+ “non‐converters” still showed longitudinal cognitive decline (80% in PREVENT‐AD and 50% in HABS), suggesting likely future clinical progression (Figures 4 and 5). By contrast, cognitive trajectories in the A+T‐, A‐T+, and A‐T‐ groups remained predominantly stable. Evidence of neurodegeneration increased MCI conversion rates in the A+T+ group to 75%, though ∼33% of A+T+N‐ individuals also progressed (Figure 6).ConclusionAmong two cohorts of cognitively unimpaired older individuals, abnormality in both amyloid‐ and tau‐PET biomarkers was associated a dramatic increase in MCI progression within a short period of time as compared against individuals having only amyloid pathology or no pathology. These findings support the clinical prognostic relevance of a biological definition of AD in individuals without cognitive impairment.
Incorporating history into disparities research: A model for interdisciplinary collaboration and trust building
Authors: Smith Kylie M , Jeong Leanne , Hu William T. , Wharton Whitney 
Published: December 2022

AbstractBackgroundTrust and mistrust of the healthcare system by Black and African American individuals is often cited as a primary barrier to willingness to access healthcare and low representation in clinical research. The United States Public Health Service Syphilis Study at Tuskegee is most often cited as the sole reason for this mistrust, however similar and widespread injustices, as well as the history of segregated health systems particularly in the stroke belt, laid the foundation for current health and research inequities. Medical discourses and practices positioned the Black or African American body as “inferior” and created disparities in research, diagnostic practices, and service provision.MethodOur team collaborated with a historian of psychiatry and disability to document the historical factors that have created structural inequities in the South and beyond. The historian (KS) collected archival material related to Milledgeville Central State Hospital in Georgia including physical documents like annual reports, case studies, policies and procedures, and letters from patients. Annual reports which contained extensive diagnostic data were photocopied, computerized and analyzed to characterize patient diagnosis (mental, physical, age‐related), cause of death, and cost expended per patient, by race and gender.ResultsOur data documents the impact of segregated services, racist medical attitudes, abusive treatments, malpractices, physical labor exploitation, unlivable conditions, use of unapproved drugs, and involuntarily sterilizations in B/AA patients from 1956‐1970. Significant diagnostic disparities by race were obvious especially for “cerebral arteriosclerosis” and “schizophrenia”. Here we present data on first admissions diagnosis by age, gender, and race, as well as death rate and cause, and group level fiscal reports.ConclusionAn analysis of historical data demonstrates clear linkages with current issues and practices. Addressing structural racism in research, education and service provision requires an honest reckoning with past practices that persist today. We demonstrate the ways that scientific teams can work with trained historians to unpack the consequences of past policies and set out a method and agenda for collaborative interdisciplinary research moving forward.