bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring the role of sleep in Alzheimer’s disease: The AlfaSleep project
Authors: Fauria Karine , Minguillon Carolina , Stankeviciute Laura , Hernández Laura , Knezevic Iva , Deulofeu Carme , Fuentes‐Julian Sherezade , Turull Israel , Fusté David , Sánchez‐Benavides Gonzalo , Arenaza‐Urquijo Eider M , Suárez‐Calvet Marc , Iranzo Álex , Gispert Juan Domingo , Molinuevo Jose Luis , Grau‐Rivera Oriol ,   ALFA Study
Published: December 2022

AbstractBackgroundSleep disruption is prevalent in the aging population and increasingly recognized as a risk factor and an early sign of Alzheimer’s disease (AD). Emerging data suggest that dysregulated orexin signaling may play a role in this association. The AlfaSleep study aims to characterize sleep patterns with objective measurements, and determine CSF orexin‐A levels in cognitively unimpaired middle‐aged adults at increased risk of AD.MethodTwo‐hundred cognitively unimpaired middle‐aged adults from the prospective observational ALFA+ study have been invited to participate in this study based on their cerebrospinal fluid (CSF) biomarker profile at the ALFA+ study baseline (100 in the AD continuum and 100 control, non‐altered biomarkers). ALFA+ participants are followed‐up every three years (and currently are undergoing the first follow‐up visit) with neuropsychological testing, blood and CSF sampling, and MRI and PET (with FDG, amyloid and tau tracers) acquisition. Participants included in the AlfaSleep study are additionally characterized with actigraphy (Actiwatch2®, Philips Respironics during two weeks), a nasal flow monitoring device (RUSleeping RTS®, Philips Respironics during one night) and CSF orexin‐A measurements. Furthermore, a subset (n = 90) will undergo overnight polysomnography at the Hospital Clinic Sleep Unit (Figure 1). Sleep‐related outcomes include total sleep time, sleep latency, sleep efficiency, sleep fragmentation, apnea‐hypopnea index and sleep microarchitecture measures (e.g. spectral power analyses). These data, as well as CSF orexin‐A levels will be compared across participants with different AD biomarkers profiles and correlated with cognitive performance, neuroimaging and fluid biomarkers data.ResultTo date, 134 participants (mean age 64.4 years, 67% female) have been included in the AlfaSleep study. Of these, 50 (37.9%) reported poor sleep quality (PSQI>5), 45.5% are APOE‐ε4 carriers and 35.8% had abnormal CSF amyloid levels, as measured at the ALFA+ baseline visit (see Table 1 for a detailed description).ConclusionDue to its multimodal approach, and the profile of the study population (cognitively unimpaired middle‐aged adults in the AD continuum), the present project will make relevant contributions to understand the role of sleep disruption as a risk factor and early clinical manifestation of AD. Preliminary results will be presented at the conference.
Multiparity and brain‐age interact with amyloid aggregation to determine cognitive performance in cognitively unimpaired middle‐aged women
Authors: Brugulat‐Serrat Anna , Cumplido‐Mayoral Irene , Sánchez‐Benavides Gonzalo , Operto Greg , Falcon Carles , Cacciaglia Raffaele , Shekari Mahnaz , Carmona Susana , Minguillón Carolina , Fauria Karine , Molinuevo Jose Luis , Grau‐Rivera Oriol , Arenaza‐Urquijo Eider M , Suárez‐Calvet Marc , Gispert Juan Domingo ,   for the ALFA study
Published: December 2022

AbstractBackgroundHow pregnancy influences women's brain aging later in life remains controversial. Whereas multiparity has been associated with decelerated brain aging, it has been also identified as a risk factor for Alzheimer’s disease (AD). However, the impact of of childbirths on cognitive performance in women at higher risk of developing AD is underresearched. We investigated whether the association between multiparity, brain‐age and amyloid (Aβ) aggregation contributes to cognitive performance in cognitively unimpaired (CU) middle‐aged women.MethodWe included 223 CU parous women from the ALFA+ study (age 49‐74) who underwent cognitive assessment and had valid T1w MRI, and [18F]‐flutemetamol Aβ PET. As controls, 152 men were included. Parity was categorized as 1/≥2 children. The difference between chronological and biological brain age (brain‐age delta) was estimatied by predicting the latter using a pretrained machine learning algorithm on cerebral morphological measurements. Aβ‐positivity was defined as Aβ PET ≥ 30 Centiloids. The Preclinical Alzheimer Cognitive Composite (PACC) was computed. PACC was the dependent variable in linear models using the parity, brain‐age, and Aβ status as predictors. Age, education, age at first birth, and Body Mass Index (BMI) were used as covariates. Interaction term with the parity*brain‐age*Aβ status was modeled.Result25.7% of women had one child (n=7 [12.3%] Aβ‐positive), and 74.3% had two or more children (n=25 [15.1%] Aβ‐positive). There were no significant differences in PACC (p=0.701) or Aβ status (p=0.071). Multiparity women showed a significant decelerated brain‐aging (p=0.017) than women with one childbirth. A parity*brain‐age*Aβ status interaction (p=0.021) revealed that among women with one childbirth, an accelerated brain‐aging was associated with a worse PACC performance in those that are Aβ‐positive (p=0.008), while this association was not found in Aβ‐negative (p=0.702) (Figure 1). The same interaction was not found among men (p=0.234).ConclusionOur results suggest that the interaction between parity, brain‐age, and Aβ aggregation contributes to cognitive performance in women later in life. Multiparity might exert a neuroprotective effect in cognitive performance in women at higher risk of developing AD. This finding represents an important contribution to the knowledge of female‐specific AD risk factors and women’s brain aging.
Intrusive thoughts and distress following amyloid status disclosure in a preclinical Alzheimer’s disease trial: Comparisons across racial and ethnic groups
Authors: Ritchie Marina , Gillen Daniel L. , Grill Joshua 
Published: December 2022

AbstractBackgroundPreclinical Alzheimer’s disease (AD) trials require biomarker testing and disclosure. Efforts are underway to improve the diversity and inclusivity of enrollment in these trials. While previous studies have shown that disclosure can be performed safely, trialists need a more thorough understanding of whether reactions to AD biomarker disclosure differ among racial and ethnic groups.MethodsUsing data from the Anti‐Amyloid treatment in Asymptomatic AD (A4) study, we analyzed participant responses on the Impact of Events Scale (IES), a 15‐item questionnaire that assesses avoidance and intrusive thoughts after disclosure of amyloid imaging results. We fit a linear regression model to test the hypothesis that mean IES scores differed by racial and ethnic group. Confounding factors included age, sex, years of education, study partner type, family history of AD, and amyloid status. We considered potential effect modification by amyloid status by including an interaction term between race and amyloid group.ResultsIES data were available for 4311 participants (3795 = non‐Hispanic [NH] White; 147 = NH Black; 165 = NH Asian; 135 = Hispanic; 69 = Other). Figure 1 illustrates total IES scores for the groups, stratified by amyloid status. NH Asians demonstrated the highest mean IES among those with elevated amyloid. Hispanics demonstrated the lowest mean IES among those with elevated amyloid, but the highest among those with not elevated amyloid. In a regression model, younger age, female sex, and family history of AD were associated with higher IES. Overall, the association between race and ethnicity groups and IES did not significantly differ by amyloid status (p = 0.200). Among not elevated amyloid individuals, Hispanics had a significantly higher mean IES when compared to NH Whites (est: 2.38; 95% CI: 0.57, 4.19; p = 0.10). Among elevated amyloid individuals, this estimate decreased to ‐1.47 (95% CI: ‐4.36, 1.42; p = 0.319).ConclusionsWhen stratified by amyloid status, all racial and ethnic groups showed higher mean IES in the elevated compared to the not elevated amyloid group, except for Hispanics. This may suggest that, for Hispanics, having an AD biomarker test at all may lead to avoidance and intrusive thoughts, regardless of the specific test result.
Diversity in Alzheimer’s Disease drug trials: The importance of eligibility criteria
Authors: Franzen Sanne , Smith Jade , van den Berg Esther , Mindt Monica Rivera , van Bruchem‐Visser Rozemarijn L. , Abner Erin L , Schneider Lon S. , Prins Niels D. , Babulal Ganesh M , Papma Janne M. 
Published: December 2022

AbstractBackgroundTo generalize safety and efficacy findings, it is essential that diverse populations are well represented in global Alzheimer’s disease (AD) drug trials. In this review, we aimed to 1) investigate participant diversity in disease‐modifying AD trials over time, 2) identify which eligibility criteria were used and how they were defined, and 3) how the eligibility criteria may have affected participant diversity levels.MethodA systematic review of amyloid and tau trials in Alzheimer’s disease was performed using Medline, Embase, the Cochrane Library, and Clinicaltrials.gov, identifying 2247 records.ResultIn the 101 included AD trials, participants were predominantly white (median percentage: 94.7%, IQR: 81.0%‐96.7%); and this percentage showed no significant increase or decrease over time (2001‐2019). Eligibility criteria such as exclusion of persons with psychiatric illness (78.2%), cardiovascular disease (71.3%) and cerebrovascular disease (68.3%), obligated caregiver attendance (80.2%), and specific MMSE‐scores (90.1%; no significant increase/decrease over time, Fig. 1) may have led to a disproportionate exclusion of ethnoracially diverse individuals. Furthermore, many eligibility criteria—such as those describing diabetes, hepatic disease, and renal conditions—often were not well defined, or the cut‐offs varied considerably across studies.ConclusionEthnoracially diverse participants continue to be underrepresented in AD clinical trials. Several recommendations will be provided, such as the use of internationally recognized clinical classifications and cut‐offs, inclusion of participants with specific medical conditions as long as they are medically stable, and flexibility in requirements regarding caregiver attendance. Furthermore, changes need to be considered to 1) the instruments used in trials (e.g. overreliance on MMSE) and 2) requirements regarding education, literacy, and language.
Initial Correlation Analysis of Diffusion Basis Spectrum Imaging of Alzheimer's Brain and Quantitative Histology
Authors: Wu Wenjie , Wang Qing , Sun Zhexian , Flores Shaney , Hobbs Diana A. , Franklin Erin E. , Perrin Richard J. , Benzinger Tammie L.S. , Wang Yong 
Published: December 2022

AbstractBackgroundDiffusion Basis Spectrum Imaging (DBSI), a novel MR imaging method, can be utilized as a biomarker in Alzheimer's disease (AD) staging and progression. By distinguishing diffusion effects from other concomitant disorders like axonal injury/loss and demyelination, it is capable of specifically evaluating cellularity and edema in AD brains. To test the hypothesis that DBSI cell fraction and mean cell apparent diffusion coefficient (ADC) correlate with measures of cell density and neuroinflammation, we employed high‐resolution ex vivo DBSI sequences and quantitative digital histology on post‐mortem human AD brain tissue.MethodA 15 × 20 × 6 mm3 sample of middle frontal gyrus (coronal plane) from a formalin‐fixed post‐mortem human AD brain (Knight ADRC) was incubated in phosphate‐buffered saline for 48 hours and then imaged with a Varian 11.7T MRI, using 2D spin‐echo sequence with 20‐direction diffusion encoding (max b = 3900 s/mm2, voxel = 0.25 × 0.25 × 1 mm). After MRI, it was precisely bisected along the plane of imaging, and processed into paraffin. Slide‐mounted 6 µm histologic sections were stained with H&E and DAB immunohistochemistry (IBA‐1 antibody, hematoxylin counterstain) and digitized (20× magnification, Hamamatsu NanoZoomer). Maps of nuclei and microglia density were computed by segmenting nuclei and microglia respectively using Orbit Image Analysis and in‐house MATLAB programs. These histology maps were registered and downsampled to the corresponding DBSI maps. We generated cell fraction maps and mean cell ADC maps from MR images using in‐house DBSI software by computing the fraction and mean ADC of restricted isotropic diffusion (0 – 0.4 ×10‐3 mm2/s in each image voxel, respectively.ResultAmong 71 evenly spaced regions of interest (ROIs, 2 × 2 mm2) drawn across the entire specimen, the mean DBSI cell fraction correlated with nuclei density (R = 0.74, P < 0.001). Additionally, among 42 ROIs drawn in the white matter region, the mean cell ADC in each ROI correlated with microglia density (R = 0.54, P < 0.001).ConclusionOur findings demonstrated the feasibility of DBSI technique in ex vivo AD brain, which could improve our understanding of neuroinflammation in AD pathogenesis, early diagnostics and new disease‐modifying therapies targeting the immune response.
Dementia vs Depression : new methods for differential diagnosis using automatic speech analysis
Authors: Labbé Céline , König Alexandra , Lindsay Hali , Linz Nicklas , Tröger Johannes , Robert Philippe 
Published: December 2022

AbstractBackgroundDepression is a very frequent pathology among elderly people. Depressive symptoms are common in neurocognitive disorders which can make it challenging to timely detect and diagnose them. Indeed, there is a high comorbidity of early‐stage dementia and late‐ life depression. There are only a few diagnostic tools that help support differential diagnosis. Speech analysis and the extraction of vocal and linguistic markers could be a non‐invasive new method to support this process.MethodsIn this pilot study, we included 12 female patients aged over 60 diagnosed with depression without a neurodegenerative disease or other psychiatric disorders. Clinical assessments include the MMSE, MADRS, EGF and ADC. They were asked to perform two short cognitive vocal tasks: a verbal semantic fluency (SVF) and a short narrative task (positive/negative storytelling) which were recorded. Then, in a second step, vocal and prosodic features were extracted from the audio files using machine learning methods. In a final step, we compared these features to those from a cohort of 9 matched patients with Mild Cognitive Impairment (MCI) diagnosis without any mood disorder and 21 matched controls.ResultsA non‐parametric kruskal‐wallis and BH‐adjusted post‐hoc wilcox test analysis revealed a statistically significant (p<0.05) lower word and semantic switch count for MCI compared to depression in the SVF. Linear mixed effects models were used to evaluate 18 prosodic features from the narrative speech task. Four features were significant between diagnoses; sound to noise ratio, delta deltas and deltas were significantly different for the depressed group in comparison to HC and MCI; total phonation time was longer for depressed patients than MCI patients in the negative story.ConclusionAutomatic analysis of vocal tasks such as semantic verbal fluency and storytelling could be a fast solution for supporting early characterization of patients. Certain types of extracted speech features show different patterns and namely prosodic features should be further explored. To conclude, this new method could serve as an objective assessment tool for differentiating depression from mild cognitive impairment.
Functional eigenvector centrality dynamics are related to amyloid deposition in preclinical Alzheimer’s Disease
Authors: Lorenzini Luigi , Ingala Silvia , Collij Lyduine E. , Wottschel Viktor , Haller Sven , Blennow Kaj , Frisoni Giovanni B , Chetelat Gael , Payoux Pierre , Martinez‐Lage Pablo , Ewers Michael , Waldman Adam , Wardlaw Joanna M , Ritchie Craig W. , Gispert Juan Domingo , Mutsaerts Henk‐Jan , Visser Pieter Jelle , Scheltens Philip , Tijms Betty M. , Barkhof Frederik , Wink Alle Meije 
Published: December 2022

AbstractBackgroundIn preclinical Alzheimer’s disease (AD), amyloid accumulates in highly‐functionally connected brain regions. This selective vulnerability is related to the high neuronal fluctuations, typical of these regions. Dynamic functional connectivity (FC) was introduced to investigate network organization over time, with high network variations indicating regional flexibility, hence promoting functional integration. The relation of early amyloid deposition with FC dynamics remains unclear. Eigenvector centrality (EC) evaluates a node's importance in functional networks, both for the whole functional MRI time series (“static” EC) or within sliding‐windows (“dynamic” EC). We studied the association of cerebrospinal fluid (CSF) amyloid load with static and dynamic EC in non‐demented individuals from the European Prevention of Alzheimer’s Dementia (EPAD) cohort.MethodsData for 701 non‐demented participants were available. CSF Aβ1‐42 levels <1000 pg/mL were defined as amyloid positive (A+) (Elecsys assay). Both static and dynamic voxel‐wise EC were computed from rs‐fMRI time series. Static EC differences between A+ and A‐ participants were assessed using linear models. Standard deviation and range of dynamic EC were compared between A+ and A‐ participants within significant clusters of static EC differences, and within 10 resting‐state networks (RSN). Linear models were used to determine interaction between amyloid and cognitive performance on dynamic EC variability (Figure 1).ResultsDemographics and clinical characteristics are shown in Table 1. A+ participants showed lower static EC values in parietal and occipital clusters, and higher static EC values in a medio‐frontal cluster (Figure 2). The medio‐frontal cluster had also lower dynamic EC variability in A+ (Figure 3). The default mode (Figure 4) and the visual networks of A+ participants also showed lower dynamic EC variability (p < 0.001). Dynamic properties in the DMN and visual networks were differentially associated with cognitive domains in the A‐ and A+ groups, with lower variability found in A+ participants with higher cognitive scores (Figure 5).ConclusionWe demonstrate that early amyloid deposition affects static and dynamic EC, possibly by reducing involvement of functional hubs in different network dynamics and functional integration, thus relating to cognitive dysfunctions. Our data suggest dynamic EC as an early biomarker of the interplay between early amyloid deposition and cognitive decline.
Alzheimer’s Disease and Related Dementias Referral Outcomes among Diverse Rural Residents
Authors: Wiese Lisa Ann Kirk , Holt Janet , Williams Ishan Canty , Group Research 
Published: December 2022

AbstractBackgroundThis study to increase dementia detection in rural settings was conducted in a medically underserved area of southcentral Florida referred to as “the Glades.” Residents face multiple disparities related to Alzheimer’s disease and related dementias, including less than a 60% high school graduation rate, diabetes and hypertension as the primary health diagnosis, and increased exposure to ambient air particles from sugar cane burning.MethodLocations for testing included eight churches and a virtual “location,” for a total of ten sites. Individuals (N = 217) were cluster sampled within these sites. Participants were administered a brief educational intervention and brain health was screened using Nasreddine’s telephone “Mini‐MoCA” (2019), in which a score < 12 indicates cognitive risk. Referrals were determined for 133 participants.ResultsThe sample consisted of 59 virtual and 74 in‐person participants, with a range of 1 to 14 participants per church. Seventy percent were female, 57% lived alone, 13% had a previous memory test, and less than 1% had been previously diagnosed with dementia. The sample consisted primarily of four ethnicities, n = 44 (33%) African Americans, n = 21 (16%) Afro‐Caribbeans, n = 19 (14%) Hispanics, and n = 45 (34%) Whites.There was substantial variance in outcomes across sites, with an ICC = .179 (n = 133), indicating that 17.9% of the variance in referrals was between sites. Therefore, a mixed linear model approach was used for analysis with participants nested within sites (Feaster et al., 2011) The covariate of whether they had a previous memory test was not statistically significant and only reduced the ICC by .002. Neither gender, nor whether the person lived alone were statistically significant predictors of referrals, (p > .05), however adding ethnic group reduced the ICC to .102, a 47% reduction in intercept variance (N = 131). In particular, African American participants were significantly more likely to have referrals than White participants, g20 = 1.29, p<.001, OR = 3.66. Hispanic and Afro‐Caribbean participants were not significantly more likely to have referrals than White participants, p = .52 and p = .06 respectively.ConclusionFurther investigation is needed to determine place‐based factors that uniquely increase risk for African Americans in an 89% racially/ethnically diverse community.
The relationship between study partner type and rate of AE reporting in mild‐to‐moderate AD clinical trials
Authors: Lu Thuy Vu Dieu , Gillen Daniel L. , Grill Joshua 
Published: December 2022

AbstractBackgroundIn randomized clinical trials (RCTs), monitoring adverse events (AE) and serious AE (SAE) is critical to maximize patient safety and trial integrity. There are, however, instances where the propensity to report AEs may differ by participant characteristics, thereby altering the observed safety profile of interventions and the generalizability of safety results. All Alzheimer’s disease (AD) clinical trials require participants to enroll with a study partner (SP), who ensures trial compliance and serves as a source of study data, including data on AEs. We examined AE reporting patterns among AD trial participants by SP type with the hypothesis that the rates of reported AEs and SAEs differ by SP type.MethodWe conducted a retrospective analysis of combined placebo data from seven independent RCTs conducted by the AD Cooperative Study (ADCS) (Docosahexaenoic Acid, Vitamin B, Huperzine A, Rofecoxib or Naproxen, Resveratrol, Simvastatin, and Valproate). Together, these seven trials provide data on 934 patients with mild‐to‐moderate AD. SP of each participant was categorized as spouse, adult child, or other. The total number of AEs by severity and SAEs were recorded for all individuals in the trial (Table 1). We also considered subject‐level reporting rates where we computed the number of unique participants reporting such events (Table 2). Chi‐squared tests were performed to determine if the rate of reporting AEs and SAEs by participants varied across SP type.ResultA total of 608 (65%) participants enrolled with spousal, 254 (27%) enrolled with adult child, and 72 (8%) enrolled with other SP. Overall, 854 (91%) participants reported at least one AE. There were 5430 AEs (66% grade 1, 28% grade 2, 6% grade 3, and 0% of grade 4 or 5) recorded. Chi‐squared tests comparing the rate of reporting by SP type yielded p‐values of 0.16 and 0.13 for AEs and SAEs, respectively.ConclusionWe observed that the reporting rate of AEs and SAEs did not substantially vary by SP type. This relationship was consistent across AE severity grades. Our results indicate little evidence that the reported safety profile in mild‐to‐moderate AD trials differs by SP type.