bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cardiovascular and Metabolic Disorders in Mild Cognitive Impairment among Older African‐American Adults
Authors: Turaani Mohammad , Hanna Sophie , Adams Hannah , DiCerbo Loraine M. , Martens Nancy , Kero Katherine , Giordani Bruno , Kavcic Voyko 
Published: December 2023

AbstractBackgroundMild Cognitive Impairment (MCI) is one of the early predictors of Alzheimer’s disease (AD) and dementia. Assessing comorbidities associated with an MCI diagnosis is crucial not only for diagnostic accuracy but also for understanding the role of comorbidities for evident cognitive impairment. In this study we investigated the association between MCI (amnestic and/or non‐amnestic) and primary comorbidities of hypertension (HTN), hyperlipidaemia (HLD), and Diabetes Mellitus (DM).MethodsWe recruited 116 community‐dwelling older African American adults with subjective complaints (103 females, 13 males, mean age = 73.93, range = 65‐91) through the Wayne State Institute of Gerontology and Michigan Alzheimer’s Disease Research Center. Participants were consensus diagnosed as 57 healthy controls (HC), 32 amnestic MCI (aMCI), and 27 nonamnestic MCI (naMCI) based on NAAC Unified Data Set3 protocol. NIH Toolbox‐Cognition battery (NIHTB‐C) also was administered, but not used for consensus.ResultsAcross all the participants, UDS3 forms noted 69% with hypertension (HTN), 56% hyperlipidaemia (HLD), and 28% Type II Diabetes. Among the normal group: 65% had HTN, 51% had HLD, and 28% had DM. Among those diagnosed with aMCI: 66% had HTN, 53% had HLD, and 34% had DM. Among naMCI: 81% had HTN, 63% had HLD, and 22% had DM. Comorbidities did not differ significantly across diagnostic groups. Regarding NIHTB‐C, significant ANOVA interactions did show that aMCI and naMCI with DM were lower in Fluid Intelligence than scores for controls with DM (p = .017). Also, as compared to controls with HTN, naMCI with HTN were lower on Fluid Intelligence (p = .024).ConclusionOur findings did not demonstrate differences in cardiovascular‐ or diabetes‐related comorbidities across MCI groups and controls in this group of community‐dwelling African Americans. However, DM and HTN, but not HLD, did affect the relationship among the diagnostic groups for fluid intelligence, a summary measure of higher cognitive functioning. This suggests that comorbid medical factors can affect cognitive performance, particularly for MCI groups. Further research is needed to fully understand the scope of risk factors influencing cognitive decline in African Americans. Further analyses will assess relationships between cognitive diagnoses, race, health conditions, and demographic risk factors.
Multiomics Blood Biomarkers Predict Alzheimer from predementia with High Specificity
Authors: Souchet Benoit , Michaïl Alkéos , Heuillet Maud , Dupuy‐Gayral Aude , Haudebourg Eloi , Pech Catherine , Berthemy Antoine , Billoir Baptiste , Autelitano François , Fortea Juan , Fowler Christopher J , Jayadev Suman , Lleo Alberto , Masters Colin L , Mouton‐Liger Francois , Paquet Claire , Braudeau Jerome 
Published: December 2023

AbstractBackgroundThe core criteria for mild cognitive impairment (MCI) or dementia due to Alzheimer’s disease (AD) is based in part on cognitive criteria and biological evidence of at least moderately frequent amyloid plaques (Albert et al., 2011; McKhann et al., 2011). They predict AD among cognitively impaired patients with high sensitivity but low specificity (Kokkinou et al. 2021; Martinez et al. 2017; Ritchie et al. 2014): up to 25% of amyloid‐positive patients would be misdiagnosed as AD while suffering from another brain disorder. Therefore, there is a need to develop new biomarkers with high specificity to facilitate earlier diagnosis, rapid therapeutic intervention and monitoring of clinical trials.MethodTargeted mass spectrometry assays were developed for 81 blood biomarkers (45 proteins and 36 metabolites) pre‐identified in AAV‐AD rats (Audrain et al. 2018). 345 cognitively impaired participants (193 MCI and 152 demented) were collected in plasma at baseline and followed clinically from 1 to 13 years. Participants’ diagnosis (AD, or non‐AD) was established at the last clinical visit based on cognitive diagnosis standard for each brain disorder subtype. 82.9% of participants had available amyloid status at baseline, 61.9% were determined as amyloid positive. Each clinical group contained amyloid positive and negative participants. Predictive machine learning models for AD participants (n = 123 Prodromal AD, n = 126 AD dementia) among individuals with Non‐AD brain disorder (Non‐AD, n = 96) was developed. The training dataset (70%) aimed to select the biomarkers subset, train the algorithm, and define the cut‐off. External test dataset (30%) aimed to validate in blind the locked predictive model.ResultWith 19 blood biomarkers and age, the model predicted AD participants (n = 41 Prodromal AD, n = 43 AD dementia) from non‐AD participants (n = 25) with 92.0% specificity and 52.4% sensitivity during external validation (AUROC = 71.8%, p = 0.001). When amyloid status (CSF or PET) and this predictive ML model were applied in series, and participants positive for both were predicted as AD, 100% specificity and 39.7% sensitivity were achieved.ConclusionMultiomics blood peripheral biomarkers predicted AD patients among cognitively impaired population with low FPR. In combination with amyloid screening, these biomarkers could identify a nearly pure AD population.
Associations between NIH‐Toolbox Flanker test and the Picture Sequence Memory Test and multimodal Alzheimer’s disease biomarkers in cognitively unimpaired individuals
Authors: Samaniego‐Reinoso Melissa , Sánchez‐Benavides Gonzalo , López‐Martos David , Suárez‐Calvet Marc , Gispert Juan Domingo , Garcia Marina , Carboni Margherita , Kollmorgen Gwendlyn , Minguillon Carolina , Zetterberg Henrik , Blennow Kaj , Grau‐Rivera Oriol 
Published: December 2023

AbstractBackgroundAlzheimer’s disease (AD) pathologic changes start years before cognitive symptoms appear; however, some early cognitive changes can be detected. We aimed to investigate the association between performance changes in two NIH‐Toolbox tests, Flanker test and Picture Sequence Memory Test (PSMT), and baseline AD biomarkers in cognitively unimpaired (CU) individuals.MethodWe studied 356 ALFA+ participants (mean [SD] age = 60.8 [4.72] years). The rate of cognitive change after 3 years was measured through delta scores (Follow‐up ‐ Baseline). Biomarker data included cerebrospinal fluid (CSF) β‐amyloid (Aβ) 42/40 ratio, phosphorylated‐tau‐181 (p‐tau), neurofilament light (NfL), and neuroimaging (MRI‐based cortical thickness AD signature, hippocampal volumes, and FDG‐PET‐SUVR (fluorodeoxyglucose ‐ positron emission tomography ‐ standardized uptake value ratio) metaROI) measures (Table 1). CSF Aβ40, Aβ42 and NfL were measured with the exploratory NTK robust immunoassays, while CSF p‐tau181 was measured using the electrochemiluminescence Elecsys® Phospho‐Tau (181P) CSF immunoassays (Roche Diagnostics International Ltd). AT groups were defined according to published cut‐offs (A+ Aβ 42/40 <0.071; T+: p‐tau > 24 pg/ml) (Milà‐Alomà et al., 2020). Baseline biomarker levels were used as predictors of cognitive change in linear models. Interactions and stratified analyses were also explored.ResultAT groups significantly differed in the change in the Flanker Test (p = 0.002), driven by poorer performance in A+T+ vs A‐T‐ groups (p = 0.001) (Figure1). Higher cortical thickness predicted better longitudinal cognitive performance in the Flanker Test in the whole group (p = 0.020; β = 0.983). We found a significant interaction between AT group and FDG‐PET‐SUVR (p = 0.042), and the right hippocampal volume (p = 0.001), but no significant associations were observed in analyses stratified by AT (Figure2A). A negative association between right hippocampal volume and Flanker test change was observed in A+T‐ (p = 0.026) and A+T+ (p = 0.029) (Figure2B). No associations were observed between the PSMT and AD biomarkers (Figure 1).ConclusionOur findings support the hypothesis that executive function show a steeper decline than episodic memory in CU individuals with evidence of pathologic AD biomarkers levels. We observed a negative association between executive change and neuroimaging neurodegeneration markers in A+T‐ and A+T+ groups. This is in accordance with previous studies that found non‐linear associations in incipient AD pathology.
Associations between gait velocity parameters and brain amyloid‐β levels in cognitive normal older adults: a cross‐sectional analysis from the AGUEDA trial
Authors: Martín‐Fuentes Isabel , Solis‐Urra Patricio , Shekari Mahnaz , Ruiz‐Malagón Emilio J. , Rivas‐Navas Daniel , Sclafani Alessandro , Sánchez‐Aranda Lucía , García‐Rivero Yolanda , Gispert Juan Domingo , Erickson Kirk I. , Esteban‐Cornejo Irene 
Published: December 2023

AbstractBackgroundAmyloid beta (Aβ) is a protein associated with dementia‐related pathologies such Alzheimer’s Disease (AD). The accumulation of Aβ in the brain leads to neurodegeneration, cognitive decline, and motor impairment. Those factors might appear years before the onset of apparent symptoms. There is evidence for an association between gait performance and Aβ levels. Previous studies showed that gait variability has been associated with amyloid in older adults with mild dementia. Moreover, reduced gait velocity, has been associated with greater brain Aβ accumulation in adults with spontaneous memory complaints. However, the nature of this relationship needs further investigation. Thus, the aim of this study was to examine the associations between gait velocity and gait velocity variability, and brain amyloid burden in cognitively normal older adults. Our hypothesis was that gait parameters might differently predict amyloid‐β levels.MethodEighty‐six cognitively normal older adults participated in this study. The Optogait system (Microgate,Italy) was used to assess gait parameters. Participants walked back and forth across a 5‐m long track made up of two bars set 1‐meter apart at their maximum walking pace for 5 minutes. The mean and coefficient of variation (CV) of gait velocity (m/s) were calculated as gait parameters. All participants underwent T1‐weighted MRI scans and [18F] Florbetaben amyloid‐PET (positron emission tomography) scans. PET images were quantified using the Centiloid (CL) method with a 12 CL cutoff for amyloid positivity. The associations between gait parameters and amyloid burden were assessed by individual linear regressions unadjusted and adjusted by age, sex, and education as covariates.ResultTable 1 shows descriptive characteristics of the participants (22% Aβ+; 19 participants). Results showed a positive trend for an association between gait variability and amyloid burden (unadjusted: β = 0.21, p = 0.05; adjusted: β = 0.20, p = 0.08). There was no association between gait velocity and amyloid burden (unadjusted: β = ‐0.08, p = 0.42; adjusted: β = ‐0.08, p = 0.54).ConclusionGreater gait velocity variability, but not gait velocity, was marginally associated with higher amyloid burden. This result suggests that gait velocity variability might be an important marker of amyloid burden in cognitively normal older adults.
Levels of nicastrin species are increased in Alzheimer’s disease cerebrospinal fluid
Authors: Hernández‐Villamizar Luis Felipe , García‐Ayllón María‐Salud , Pérez‐González Rocío , Zetterberg Henrik , Blennow Kaj , Sáez‐Valero Javier 
Published: December 2023

AbstractBackgroundPrevious studies demonstrated that presenilin‐1 (PS1), the active component of the intramembrane γ‐secretase complex, can be detected as soluble heteromeric aggregates in cerebrospinal fluid (CSF). The γ‐secretase components, APH‐1 (anterior pharynx‐defective 1) and PEN‐2 (presenilin enhancer 2), co‐exist within these CSF complexes, while nicastrin is not detected. Nicastrin plays a role in the complex regulation, and substrate recognition, being the unique γ‐secretase component that is a Type I, single‐pass transmembrane protein. Here, we aim to characterize and compare the levels of CSF nicastrin in Alzheimer’s disease (AD) and non‐AD control patients.MethodNicastrin levels were analyzed in the CSF by western blotting using antibodies against the ectodomain and the intracellular domain to identify the different species of the protein that are present in human CSF. Core AD CSF biomarkers (Aβ1‐42, T‐tau, and p‐Tau) were determined. We also assessed whether CSF nicastrin and PS1 are present in extracellular vesicles (EVs).ResultWe show that nicastrin is present in CSF as full‐length species of 130, 110, and 105 kDa. We demonstrated that nicastrin full‐length species are increased in AD patients (n = 21) in comparison with non‐AD controls (n = 21), and that the levels of the three species positively correlated with T‐tau and p‐Tau levels in AD patients. Nicastrin was also found to be present in EVs, different from PS1.ConclusionThe increased levels of nicastrin in the CSF of AD patients confirmed the potential alteration of γ‐secretase subunits during pathology progression. Further studies are required to evaluate whether these proteins could act as biomarkers to facilitate AD diagnosis and/or prognosis.
Risk Reduction: A Novel Brain Health‐focused Pathway for Patients with Mild Cognitive Impairment in Greater Manchester
Authors: Bradley Rosie , Fox Sarah , Dunne Ross , Leroi Iracema 
Published: December 2023

AbstractBackgroundStudies have indicated that addressing lifestyle risk factors in people with mild cognitive impairment (MCI), may slow symptom progression or even reduce their risk of progressing to dementia. However, this important window for dementia risk reduction and brain health promotion is often missed in existing UK clinical pathways. This work aims to develop and pilot a brain health navigation pathway for individuals receiving an MCI diagnosis from a UK memory assessment service in Greater Manchester.MethodsTo date we have worked closely with people living with MCI to develop this pathway and all associated resources and materials. In January 2023 we developed and embedded a new role, which we refer to as a brain health navigator, within an existing memory assessment service team in one commissioning region of Greater Manchester. Starting in April 2023, patients identified in memory assessments as having MCI, will receive a referral to the brain health navigation service. With support from our navigator, all those who accept the referral will receive:• A personalised brain health plan.• Brain health and prevention information and signposting to local community and professional resources.• A follow‐up appointment to assess the impact of this intervention on their wellbeing, skills, knowledge, and confidence in managing their own health.ResultsWe will use a mixed methods approach to evaluate this work. We will collect Quantitative data describing the number of patients between April and June 2023 accepting a referral into the program, the number accessing community services; and wellbeing scores (using the Warwick‐Edinburgh Mental Wellbeing scale) pre‐and post‐intervention. Qualitative feedback will also be discussed including patient and clinical reflections of the service.ConclusionsOur poster will discuss the feasibility and effectiveness of this model of patient support and how this approach may be expanded across the UK and beyond.
Preliminary evaluation of the digital maze test in relation to neuropsychological tests and AD biomarkers
Authors: Robinson Talia L , Fu Jessie Fanglu , Montenegro Grace A Del Carmen , Properzi Michael J , Klinger Hannah M , Penney Dana , Davis Randall , Sperling Reisa A. , Johnson Keith A. , Amariglio Rebecca E. , Rentz Dorene M. 
Published: December 2023

AbstractBackgroundDigital cognitive tools may provide unique opportunities to detect subtle changes in preclinical Alzheimer’s disease (AD). Here, a maze test using a digital pen was evaluated in relation to AD pathological changes measured by amyloid‐β and tau burden.Method172 participants (CN = 161, MCI = 6, dementia = 5) completed the digital maze test, which included multiple “no‐choice” (NC; i.e., no decisions required to complete maze) or “choice” (CH) conditions (i.e., problem‐solving required to complete maze). Maze composite scores were calculated using the total test duration, total number of strokes, and total pen‐off‐page time for the NC and CH conditions separately. Global amyloid‐β PET was quantified with [11C]Pittsburgh‐Compound‐B (PiB) in available participants (n = 171). Entorhinal and inferior temporal tau were quantified with [18F]Flortaucipir (FTP) in available participants (n = 135). Participants completed a battery of traditional neuropsychological tests, with domain factor scores calculated for Executive Functioning (EF), Processing Speed (PS), and Memory (Mem). The associations between maze composite scores, global amyloid‐β, entorhinal tau, and inferior temporal tau were evaluated in separate linear regression models for both the total sample and CN only, correcting for age and education.ResultIn the total sample, CH‐ and NC‐composites were moderately correlated with PS and EF factor scores (r’s = .41‐.46) and less so with Mem factor scores (r’s = 0.2‐0.35). In the total sample, higher PiB was significantly associated with worse performance in NC‐composite (β = 0.543, SE = 0.249, p <.05), but not for CH‐composite (β = 0.467, SE = 0.259, p = .07). Similar results were observed in the CN only sample (NC‐composite: β = 0.468, SE = 0.237, p <.05; CH‐composite: β = 0.469, SE = 0.278, p = .09). PiB was not associated with EF or PS factor scores in the total sample or CN only. Entorhinal and inferior temporal tau were not significantly associated with any maze composites.ConclusionIn a largely cognitively normal sample, performance on the digital maze test was associated with global amyloid‐β burden, particularly in the no‐choice condition, but not with tau. Digitally captured, nuanced performance in EF and PS may be related to early amyloid‐β pathology, but less so with downstream effects of tau.
Comparing the effectiveness of a brief observational study protocol versus a telephone call for Alzheimer’s disease prevention trial prescreening
Authors: Christiano Caitlyn , Moreno Stephanie , Anzai Juliana A. U. , Viera Andrea M Román , Palou Mariana Margarita , Kirn Dylan , Muniz Martha C. , Ramirez Jennifer , Papp Kathryn V. , Amariglio Rebecca E. , Chhatwal Jasmeer P. , Johnson Keith A. , Rentz Dorene M. , Selkoe Dennis J , McGinnis Scott M. , Marshall Gad A , Gale Seth A , Sperling Reisa A. , Yang Hyun‐Sik 
Published: December 2023

AbstractBackgroundMany Alzheimer’s disease (AD) clinical trials suffer from a high screen‐fail rate despite telephone pre‐screening, leading to a significant drain of time and resources. Thus, more efficient prescreening methods are required. Here, we examined whether prescreening through a brief on‐site observational study protocol could reduce clinical trial screen failure rate compared to telephone prescreening.MethodDe‐identified screening data from the AHEAD study (AD prevention trial) at the Brigham and Women’s Hospital (BWH) Center for Alzheimer Research and Treatment were used. Participants were referred for the AHEAD study screen either from a brief observational study protocol (Davis Memory and Aging Cohort [MAC]) or a telephone screen. MAC collects medical history, Mini‐Mental State Exam, Quick Dementia Rating System, and blood samples through a 60‐minute on‐site visit. Telephone screen uses medical history survey and Telephone Interview for Cognitive Status. We compared AHEAD screen failure rate due to (1) clinical exclusion criteria and (2) (for those with no clinical exclusion) amyloid‐β biomarker criteria (plasma, PET) between MAC and telephone screen referrals, using Fisher’s exact test.ResultThere was no significant difference in age, sex, race, or ethnicity between the MAC and Telephone prescreen groups (Table 1, MAC n = 61, telephone n = 55). The odds ratio (OR) for clinical screen‐fail for MAC (n = 9 out of 61, 15%) compared to telephone (n = 20 out of 55, 36%) was 0.31 (95% CI 0.12 to 0.80, p = 9.7×10−3). The OR for biomarker screen‐fail for MAC (n = 49 out of remaining 52, 94%) compared to telephone (n = 27 out of remaining 35, 77%) was 4.7 (95% CI 1.0 to 30.1, p = 0.024). The OR for randomization from MAC (n = 3 out of 61, 5%) compared to telephone (n = 8 out of 55, 15%) was not significantly different from 1 (p = 0.11).ConclusionOur findings show that, compared to a telephone screen, an on‐site prescreen assessing cognition, medical history, and venous access may prevent clinical trial screen fails attributable to clinical exclusion criteria and save participant/staff time and resources. However, an unexplained higher biomarker screen fail rate for the on‐site group offset this benefit, requiring further investigation.
Reduced slow‐wave sleep is associated with greater tau accumulation and reduced cortical thickness in the temporal lobe
Authors: Stankeviciute Laura , Chhatwal Jasmeer P. , Levin Raina , Pinilla Valentina , Schultz Aaron P. , Redline Susan , Johnson Keith A. , Sperling Reisa A. , Kozhemiako Nataliia , Purcell Shaun , Djonlagic Ina 
Published: December 2023

AbstractBackgroundSlow wave sleep (SWS) plays an important role in overnight memory consolidation and promotes overnight clearance of amyloid‐β and tau proteins. Reduced SWS has been associated with higher amyloid burden in CSF in preclinical stages of Alzheimer’s disease (AD). However, little is known about the relationship between SWS, tau burden and neurodegeneration in cognitively unimpaired (CU) adults. Thus, we investigated the relationships between slow‐wave sleep, regional tau PET (18F‐flortaucipir; FTP‐PET) and temporal lobe cortical thickness in cognitively unimpaired adults at risk of AD.MethodsThis study included 39 CU adults from the Harvard Aging Brain Study (mean age 74.6 years, 61.5% females) who underwent at‐home polysomnography (PSG; Compumedics Somte), tau PET (FTP‐PET, amyloid PET (PIB‐PET) and MRI (Table 1). We used separate multivariate linear regressions to assess associations between the percentage of slow‐wave sleep (SWS) and tau PET signal in the following ROIs: entorhinal cortex, inferior temporal cortex, and a temporal composite comprised of entorhinal, inferior temporal, middle temporal and fusiform cortices. Primary analyses were adjusted for age and sex. Secondary analyses also adjusted for global amyloid (PiB‐PET) burden. Lastly, we investigated the relationship between SWS and cortical thickness in the same ROIs, assessed using FreeSurfer v6.0.ResultsLower percentage of SWS was associated with greater tau burden in entorhinal (p = 0.036), inferior temporal (p = 0.011), and temporal composite ROIs (p = 0.005). Associations between reduced SWS and greater FTP‐PET signal in the inferior temporal cortex and temporal composite ROI remained after controlling for PiB‐PET (p = 0.028; p = 0.017, respectively; Table 2). Additionally, lower SWS was associated with reduced cortical thickness in the entorhinal (p = 0.031), inferior temporal (p = 0.023) and temporal composite ROIs (p = 0.002) (Table 3).ConclusionsLower SWS in CU adults was associated with higher tau burden in regions of the temporal lobe that are commonly sites of early tau accumulation. These effects were independent of amyloid burden, suggesting disrupted sleep may lead to increased tau accumulation via an amyloid‐independent pathway. Importantly, lower SWS was also associated with reduced cortical thickness in temporal regions, suggesting that disrupted sleep may be tied to early AD‐related neuronal loss in the temporal lobe.