bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Bayesian machine learning approach to Nature vs. Nurture: Predicting a cognitive marker of Alzheimer’s risk in a deeply‐phenotyped cohort of cognitively unimpaired older African Americans
Authors: Fausto Bernadette A. , Budak Miray , Sheikh Mustafa , Perez José Mojica , Li Shengguo , Hu Liangyuan , Gluck Mark A. 
Published: January 2025

AbstractBackgroundLate‐onset Alzheimer’s disease (AD) arises from interactions between genetic (nature) and environmental/behavioral (nurture) factors, but their relative contributions are not well defined. With the development of sensitive cognitive tasks for early detection and the growing applications of machine learning to AD research, there are unprecedented opportunities to uncover the most salient genetic and environmental/behavioral factors for preclinical AD risk. Our previous studies show that generalization performance––the ability to apply prior learning to new contexts––is a cognitive risk marker for preclinical AD. Using a novel technique combining Bayesian machine learning and bootstrap imputation, the present study identified among various candidate genetic and environmental/behavioral factors which most strongly predicted generalization performance in a deeply‐phenotyped cohort of cognitively unimpaired older African Americans.Method472 older African American participants from the Pathways to Healthy Aging in African Americans cohort study (Mage=68.34 years, SD=6.99; Meducation=13.93 years, SD=2.36; MMMSE=27.65, SD=1.95) responded to demographic, health, and lifestyle questionnaires; provided a saliva sample for the following AD risk genotyping: ABCA7 rs115550680, APOE‐ε4 status, ABCA7 rs3764650; and completed a cognitive battery including a generalization task (Concurrent Discrimination and Transfer Task). We employed Bayesian Additive Regression Trees (a.k.a., BART) with a bootstrap imputation‐based variable selection procedure to extract the top three variables in terms of relative contribution to generalization performance.ResultAmong 25 candidate variables, the top three ranked variables related to generalization included ABCA7 rs115550680, est.=3.29 [95% CI 0.42, 6.19]; APOE‐ε4 status, est.=2.41 [95% CI 0.37, 4.46]; and their performance on the training phase of the generalization task, est.=0.92, [95% CI 0.86, 0.99], above and beyond other demographic, health, lifestyle, and cognitive variables.ConclusionUsing novel machine learning techniques and a sensitive cognitive risk marker, we identified that generalization performance is most strongly driven by genetic vulnerability to AD among older African Americans. These findings suggest a greater contribution of genetic factors (nature) compared to environmental/behavioral factors (nurture) to late‐onset AD risk within this population.
The Neuronal Synuclein Disease Integrated Staging System ‐– Opportunities to advance Lewy body dementia research
Authors: Poston Kathleen L. 
Published: January 2025

AbstractRecent advances in biomarkers, enabling the in vivo detection of pathological aggregates of alpha‐synuclein (asyn), allow a shift from a clinical to a biological definition of Parkinson’s disease (PD) and dementia with Lewy bodies (DLB). The newly proposed “Neuronal alpha‐Synuclein Disease (NSD)” is defined by the presence of pathologic neuronal (n‐asyn) species detected in vivo (S), irrespective of any specific clinical syndrome. Additional biological anchors include dopaminergic neuronal dysfunction (D). This biological definition forms the basis for the NSD Integrated Staging System (NSD‐ISS), rooted in the biological anchors (S and D) and the degree of functional impairment caused by clinical signs/symptoms.Stage 1 and 2 are marked by S alone (Stage 1A or 2A), or both S and D (Stage 1B or 2B), differentiated by absence of signs/symptoms (stage 1) or appearance of subtle signs/symptoms without functional impairment (stage 2). Critically, these patients could progress to Stage 3 by developing a functional impairment due to cognitive or motor or other non‐motor impairments. Stages 2B‐6 require both S and D, with Stages 3‐6 defined by their stage‐specific increases in functional impairment.The NSD definition and NSD‐ISS framework will enhance drug development and clinical trial efficacy by facilitating the selection of a homogeneous target population with biological consistency, which is particularly relevant to drug development in Lewy body dementias. Also critical to research in Lewy body dementias, NSD could inform how and when to co‐enroll patients with dementia regardless whether they initially presented with motor or cognitive impairments. The NSD‐ISS framework will also context for considering research in Lewy body dementia patients with and without ATN biomarkers. Currently NSD‐ ISS is a research tool and is not intended for clinical use.
Physical activity and amyloid onset age: Findings from three studies of community‐dwelling adults
Authors: Dougherty Ryan J. , Wang Jiangxia , Bilgel Murat , Betthauser Tobey J. , Resnick Susan M. , Okonkwo Ozioma C , Johnson Sterling C. , Schrack Jennifer A , Albert Marilyn S. 
Published: January 2025

AbstractBackgroundHigher levels of physical activity, measured through accelerometry, have shown associations with several indices of brain and cognitive health in middle‐to‐late aged individuals. The association of physical activity and AD‐specific biomarkers measured through positron emission tomography (PET), however, remains unclear. Previous studies show no direct association of physical activity with amyloid‐PET levels, but associations with the onset of amyloid positivity remain unexplored. This study used estimated amyloid onset age (EAOA), defined as the age at which a participant is expected to attain the amyloid positivity threshold based on longitudinal PET, to investigate if higher levels of physical activity are associated with later EAOA across three cohort studies including: the Biomarkers of Cognitive Decline among Normal Individuals (BIOCARD) study, the Baltimore Longitudinal Study of Aging (BLSA), and the Wisconsin Registry for Alzheimer’s Prevention (WRAP) study.MethodCognitively unimpaired participants from BIOCARD (N = 133), BLSA (N = 117), and WRAP (N = 115) who completed accelerometer‐measured physical activity monitoring over 7 days and underwent 11C‐Pittsburgh compound B PET imaging were included. Total daily physical activity was measured through wrist‐worn (BIOCARD, BLSA) or waist‐worn (WRAP) accelerometers and EAOA was estimated using sampled Iterative Local Approximation. Proportional Hazard Cox regression models assessed whether physical activity is associated with EAOA, using right‐censoring for participants who did not develop amyloid positivity, adjusting for sex and APOE status. Potential interactions between physical activity and APOE status were also assessed.ResultParticipant demographics are displayed in Table 1. Across all three cohorts, APOE status was associated with EAOA (all p<.05), such that APOE‐e4 carriers had an earlier amyloid onset. We did not observe associations between physical activity and EAOA among BIOCARD (z‐score HR = 1.27, p = 0.27), BLSA (z‐score HR = 1.01, p = 0.97), or WRAP participants (z‐score HR = 0.96, p = 0.85). Furthermore, no significant interactions between physical activity and APOE status were observed (all p>.05).ConclusionAcross three separate studies of community‐dwelling middle‐ age and older adults, we did not observe an association between accelerometer‐measured physical activity and EAOA. Further research is needed to i) determine if alternate methods for assessing physical activity are associated with AD biomarkers and ii) investigate non‐AD pathways through which physical activity may impact cognitive function and AD risk.
Utilizing Advanced Artificial Intelligence for Automated Detection and Segmentation of Amyloid‐Related Imaging Abnormality (ARIA)
Authors: Xie Long , Yushkevich Paul A. , Das Sandhitsu R. , Wolk David A , Gibson Eli 
Published: January 2025

AbstractBackgroundThe recent breakthrough in monoclonal antibody treatment for Alzheimer’s disease (AD) has ushered in a new phase in AD healthcare. However, associated amyloid‐related imaging abnormalities (ARIA) present a significant risk to patients, necessitating careful monitoring. Detection by radiologists can be challenging and may suffer from inconsistency. This study investigates the potential of advanced artificial intelligence (AI) for automated detection of micro‐hemorrhage (mH) in GRE MRI and ARIA‐like abnormal hyper‐intensity in FLAIR MRI, the two primary manifestations of ARIA.MethodsIn a multi‐site clinical MRI dataset, we selected 399 GRE scans with micro‐hemorrhage as well as 161 FLAIR scans from stroke and posterior reversible encephalopathy syndrome (PRES) patients with ARIA‐like abnormal hyper‐intensity. Expert annotators, under the supervision of a radiologist, manually delineated micro‐hemorrhage and abnormal hyper‐intensity. An additional 2789 GRE scans of normal subjects were included to evaluate micro‐hemorrhage detection performance. 75% of the data was used to train two AI models to segment micro‐hemorrhage and abnormal hyper‐intensity respectively. Both AI models, adopting the U‐Net architecture, were fine‐tuned from a foundational model pretrained on another 5322 clinical MRI scans in a self‐supervised manner. Evaluation was conducted on the test set (the remaining 25%). The receiver operating characteristic (ROC) analysis was performed to investigate the discrimination of GRE scans with and without micro‐hemorrhage. Additionally, the sensitivity, and false positives of detecting individual micro‐hemorrhage were reported. For abnormal FLAIR hyperintensity, the Dice coefficient of the AI‐derived segmentation was computed.ResultsThe prototype discriminated GRE scans with and without micro‐hemorrhage with an ROC area under the curve of 0.93 (Figure 1‐d). At the single micro‐hemorrhage level, the AI system achieved a detection sensitivity of 0.81 with 0.82 false positives per scan. For abnormal FLAIR hyper‐intensity segmentation, our AI model achieved an average Dice coefficient of 0.76±0.07 (Figure 2). Qualitative results are presented in Figures 1 and 2.ConclusionsThe findings demonstrate that the proposed AI systems, grounded in foundational AI techniques, can reliably detect micro‐hemorrhage from GRE scans and segment ARIA‐like abnormal FLAIR hyper‐intensity. These systems may play a crucial role in the automated detection and monitoring of ARIA, leading to enhanced patient care.
Affective response to exercise and physical activity engagement: preliminary results
Authors: Stojanovic Marta , Morris John C. , Ances Beau , Head Denise M 
Published: January 2025

AbstractBackgroundDue to multiple benefits of sustained exercise and physical activity, it is important to understand factors that can facilitate regular engagement in physical activity in later adulthood. Prior work found that a more positive affective response to acute exercise was associated with greater physical activity levels in younger adults. However, this determinant has been understudied in older adults and individuals at risk of Alzheimer disease (AD). Hence, the first goal of the study was to examine whether affective response to acute exercise predicted physical activity levels in middle‐aged and older adults. In addition, depression has been linked to reduced physical activity levels and might impact the role of affective response. Furthermore, there is often a decline in physical activity engagement with accumulation of AD pathology. Hence, the second goal was to examine whether depressive symptoms and AD genetic risk moderate the association between affective response to exercise and physical activity levels.MethodCognitively normal individuals from the Knight ADRC Adult Children Study (n = 65; age range 44‐85 years) completed an acute bout of moderate‐intensity exercise. Affect was measured at baseline, immediately after, 15 minutes, and 30 minutes post‐exercise. Participants also completed one week of actigraphy, which provided an objective estimate of their physical activity levels. Finally, the 15‐item Geriatric Depression Scale (n = 49) and APOE phenotyping (n = 61) were obtained from a subset of individuals.ResultGreater increase in high arousal positive affect following the acute exercise was associated with higher levels of moderate‐to‐vigorous physical activity (p = 0.041). There was no moderation by depressive symptoms (p = 0.288) or APOE genotype (p = 0.962).ConclusionThese results suggest that a positive affective response to moderate‐intensity exercise might be predictive of physical activity levels across later adulthood. However, depressive symptoms and genetic risk for AD might be limited as moderators of this association. Future studies should use a larger sample size and consider additional measures of AD pathology as potential moderators.
Blood‐based biomarkers and risk of MCI symptom onset over the short and long‐term
Authors: Soldan Anja , Pettigrew Corinne , Wang Jiangxia , Albert Marilyn S. , Blennow Kaj , Bittner Tobias , Moghekar Abhay 
Published: January 2025

AbstractBackgroundBlood‐based biomarkers of amyloid and tau have been shown to predict AD‐dementia risk. Much less is known about their ability to predict risk of Mild Cognitive Impairment (MCI) among cognitively normal individuals. It is also unclear how AD non‐specific blood markers of neurodegeneration and neuroinflammation predict MCI and whether they add predictive power above and beyond AD biomarkers. The current study examined whether levels of blood biomarkers of amyloid (Ab42/Ab40), tau (p‐tau181), neurodegeneration (NfL), and neuroinflammation (GFAP, YKL40, sTREM2) collected when participants were cognitively normal predict the time to onset of MCI, both alone and in combination.MethodThe plasma assays were based on the NeuroToolKit (cobas Elecsys assays, Roche Diagnostics) and obtained from 271 cognitively unimpaired BIOCARD Study participants at their initial baseline evaluation (mean age=57.5y, including 82 who progressed to MCI/dementia). A second ‘baseline’ specimen (collected using different procedures) was evaluated for a subset of participants who were cognitively normal ∼7 years later (N=202) (mean age = 64.5y), including 31 who later developed MCI/dementia, Table 1). Mean clinical follow‐up was 15.5 years for Baseline 1 and 9.9 years for ‘Baseline 2’. Cox regression models tested the association of biomarker levels with time to MCI symptom onset, separately for both baselines.ResultsFor both baselines, lower levels of Ab42/Ab40, higher GFAP, and a higher ratio of p‐tau181/Ab42/Ab40were each associated with an earlier time to MCI symptom onset (p’s<=0.034, Table 2). For baseline 2, higher p‐tau181 (p=0.009) was also associated with earlier MCI symptom onset (p=0.009), and higher NfL was associated with earlier MCI symptom onset for progression within 7 years (p=0.05). When combining biomarkers, Ab42/Ab40 and p‐tau181 independently predicted progression (p’s <0.03), but neither GFAP nor NFL added predictive value after accounting for Ab42/Ab40 and p‐tau181. YKL40 and sTREM2 were not associated with MCI onset in any analyses.ConclusionThis indicate that during preclinical AD, more abnormal blood biomarker levels of amyloid (Ab42/Ab40), tau (p‐tau181), neurodegeneration (NfL), and neuroinflammation (GFAP) individually predict progression from normal cognition to MCI, but that AD non‐specific neurodegeneration and inflammation do not improve prediction after accounting for amyloid and p‐tau levels.
Lower plasma p‐tau levels and larger hippocampi may protect African American SuperAgers from age‐related cognitive decline and Alzheimer’s disease
Authors: Piaszczynska Wiktoria , Budak Miray , Fausto Bernadette A. , White Payton G. , Perna Robert , Fitzgerald‐Bocarsly Patricia , Tawe Fanyu Hercules , Ashton Nicholas J. , Blennow Kaj , Zetterberg Henrik , Gluck Mark A. 
Published: January 2025

AbstractBackgroundAdvancing age is typically associated with a decline in memory capacity and increased risk of Alzheimer’s disease (AD)1. However, there is a subgroup of the older adult population defined as SuperAgers that presents excellent memory performance reflecting resilience to the typical and pathological pathways of aging 2. Atrophy in hippocampal subregions, key areas for memory formation, are early anatomical markers of preclinical AD3. Plasma p‐tau levels, particularly p‐tau 231, also show sensitivity to preclinical AD neuropathology.4 This study aimed to investigate structural neuroimaging and plasma biomarker differences between the SuperAger phenotype and typical agers among African Americans.MethodParticipants were drawn from the ongoing longitudinal study, Pathways to Healthy Aging in African Americans conducted at Rutgers University–Newark. 100 participants (matched for SuperAger and typical ager status) completed a neuroimaging visit and a blood draw for plasma biomarkers. AFNI and ANTs were used for the segmentation of the medial temporal lobe and hippocampal regions of interest. Furthermore, plasma aliquots were sent for p‐tau 231 processing via Quanterix Simoa™.ResultSuperAgers had lower levels of p‐tau 231, (t(19) = 2.85, p = 0.010, d = 20.38) than typical agers suggesting reduced AD neuropathology burden. African American SuperAgers also had greater DG/CA3 volumes, (t(19) = ‐2.756, p = 0.013, d = 46.42) than typical agers.ConclusionSuperAgers demonstrated larger hippocampal subregions implicated in memory formation and lower plasma p‐tau levels suggesting potential neuroprotective mechanisms in this unique population. SuperAgers may represent a valuable model for understanding cognitive resilience and for developing novel therapeutic strategies to mitigate age‐related cognitive decline and AD.
Cognitive, behavioral, and functional outcomes of suspected mild traumatic brain injury in community‐dwelling older persons without mild cognitive impairment or dementia
Authors: Guan Dylan X. , Peters Matthew E. , Pike G. Bruce , Smith Eric E. , Ismail Zahinoor 
Published: January 2025

AbstractBackgroundTraumatic brain injury (TBI) is a risk factor for dementia and is linked to earlier age of onset. This study investigated whether later‐life changes in everyday cognition and behavior – risk markers of AD – could be observed in cognitively unimpaired older persons who sustained suspected mild TBI (smTBI) earlier in life and whether these cognitive and behavioral changes behavior mediated the link between smTBI and daily function.MethodData for 1274 participants from the Canadian Platform for Research Online to Investigate Health, Quality of Life, Cognition, Behaviour, Function, and Caregiving in Aging (CAN‐PROTECT) study were analyzed. A self‐reported brain injury screening questionnaire was used to determine smTBI. Outcomes were measured using the Everyday Cognition (ECog‐II) scale, MBI Checklist (MBI‐C), and Standard Assessment of Global Everyday Activities (SAGEA). SCD was operationalized based on a score of ≥2 (i.e., consistently a little or much worse) on any ECog‐II item. MBI+ status was defined by MBI‐C total scores ≥8. Age, sex, years of education, marital status, and ethnocultural origin were balanced across exposure groups using inverse probability of treatment weighting. Weighted logistic and negative binomial regressions were used to model smTBI (exposure) associations with SCD and MBI+ statuses, and ECog‐II and MBI‐C total scores, respectively.ResultSample characteristics are shown in Table 1. Sustaining smTBI throughout the life course was linked to a higher odds of participants reporting SCD or MBI, as well as higher ECog‐II or MBI‐C total scores [Table 2]. Finally, SCD partially mediated, whereas MBI+ fully mediated, the association between smTBI and SAGEA total scores; SCD and MBI+ statuses accounted for approximately 45% and 70% of the total effect, respectively [Table 3].ConclusionSustaining smTBI throughout the life course is linked to poorer cognition and behavior even in community‐dwelling older persons without MCI or dementia. Older persons with smTBI may benefit from early dementia risk assessment using tools that measure changes in cognition and behavior. Interventions for declining cognition and behavior may also be beneficial in this population to address functional impairment.
Matched Proteomics of Cerebrospinal Fluid and Brain Reveals Conserved and Discordant Mechanisms of Progressive Aβ Pathology in Mice
Authors: Tfaily Ali , Srivastava Upasna , Rayaprolu Sruti , Kumar Prateek , Xiao Hailian , Duong Duc M , Dammer Eric B. , Seyfried Nicholas T , Rangaraju Srikant 
Published: January 2025

AbstractBackgroundCerebrospinal fluid (CSF) is an important source of protein biomarkers for diagnosis, risk stratification, and predicting treatment response in Alzheimer's disease (AD). Proximity to brain parenchyma suggests that CSF proteomic alterations may mirror brain pathological changes. Understanding the evolution of CSF proteomic changes and their alignment with concurrent brain pathology necessitates matched CSF and brain analyses, which are possible using animal models of AD pathology.MethodCSF and brain (cortex) from 86 mice (47 wild‐type (WT) and 39 5xFAD, age groups of 1.8, 3, 6, 10, 14 months, equal males and females) underwent tandem‐mass‐tag mass spectrometry (TMT‐MS) across six TMT batches. After batch‐correction of data, we identified differentially enriched proteins (DEPs, ANOVA p<0.05), comparing WT and 5xFAD across various ages. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were conducted to elucidate conserved and discordant pathways in CSF and brain (Figure 1A).ResultOf 8,535 brain proteins and 3,721 CSF proteins, 3,236 proteins were present in both (Figure 1B). In CSF, most DEPs were found at 1.8‐10‐month ages, followed by a marked decrease at 14 months. In contrast with CSF, DEPs in brain progressively increased with age (Figures. 1C‐1D). GSVA revealed that early unique CSF changes involved synaptic dysfunction and cellular stress, while later changes involved mitochondrial dysfunction and neuronal death. In brain, our analysis indicated heightened immune/glial activation and alterations in cellular structure, with late‐stage changes indicative of cellular, synaptic, and vascular dysfunction. DEPs (5xFAD vs. CSF) in CSF minimally overlapped with brain DEPs (<10%) at 1.8 and 3 months, but increased to >30% at later (6‐14 months) time points. Concordance between CSF and brain changes was highest at 6 and 10 months, particularly in immune response mechanisms and metabolic processes (Figures. 1E‐1F).ConclusionOur findings underscore distinct patterns in the CSF and brain proteomes during the progression of Aβ pathology. Although 87% of CSF proteins are shared with brain, limited DEPs overlap shows unique molecular signatures, suggesting that most CSF changes do not reflect brain‐level changes. Despite this, immune, metabolic, and proteostasis‐related mechanisms show temporal concordance between CSF and the brain.