bluuebunny/crossref_metadata_2025_split huggingface dataset:
Relationships between PET and blood plasma biomarkers in corticobasal syndrome
Authors: Singh Neha Atulkumar , Alnobani Alla , Graff‐Radford Jonathan , Machulda Mary M. , Mielke Michelle M. , Schwarz Christopher G. , Senjem Matthew L. , Jack Clifford R. , Lowe Val J. , Kanekiyo Takahisa , Josephs Keith A. , Whitwell Jennifer L. 
Published: January 2025

AbstractBackgroundCorticobasal syndrome (CBS) is a progressive neurodegenerative syndrome that can result from several underlying neuropathologies, including Alzheimer’s disease (AD). Little is known about the utility of blood plasma measures to predict PET biomarker confirmed AD in CBS.MethodEighteen CBS patients (8 PET Aβ+; 10 PET Aβ‐) and 8 cognitively unimpaired Aβ‐ individuals (CU) were recruited by the Neurodegenerative Research group from the Department of Neurology, Mayo Clinic, Rochester, MN, underwent Aβ (Pittsburgh Compound‐B) and tau (18F‐flortaucipir) PET and provided a blood sample. Blood plasma analysis was performed to assess p‐tau 181, total tau, Aβ 42/40, neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) using the Quanterix Simoa platform. Plasma concentrations were compared between Aβ‐, and Aβ+ CBS and controls using Kruskal‐Wallis corrected for multiple comparisons using Dunn’s test, with effect size measured with area under the receiver operating characteristic curve (AUROC) analysis. Spearman correlations were calculated to assess relationships between plasma concentrations and Aβ and tau PET uptake.ResultThe CBS Aβ+ group showed reduced Aβ42/40 ratio, with elevated p‐tau 181, GFAP and NfL plasma concentrations compared to CU, while the CBS Aβ‐ group only showed elevated NfL concentration compared to CU (Figure 1). Both p‐tau 181 (AUROC=0.95) and GFAP (AUROC=0.91) plasma concentrations were able to differentiate CBS Aβ‐ from CBS Aβ+. Plasma p‐tau 181 and GFAP concentrations showed a positive correlation to both Aβ and tau PET uptake, and the Aβ42/40 ratio showed a negative correlation to Aβ PET uptake (Figure 2). No correlations were observed between NfL and Aβ and tau PET.ConclusionThis study supports the use of plasma p‐tau 181 and GFAP to detect AD in CBS, while NfL shows potential as a nonspecific disease biomarker of CBS regardless of underlying pathology. Plasma concentrations of these biomarkers may potentially be incorporated into clinical trials targeting CBS patients with underlying AD pathology.
Machine learning on unbiased proteomics of cerebrospinal fluid uncovers differential molecular signatures of Alzheimer’s disease and Normal Pressure Hydrocephalus
Authors: de Geus Matthijs B , Wu Chao‐Yi , Dodge Hiroko H , Leslie Shannon N , Wang Weiwei , Lam TuKiet , Nairn Angus C , Kivisäkk Pia , Arnold Steven E. , Carlyle Becky C. 
Published: January 2025

AbstractBackgroundThe differential diagnosis of Alzheimer’s disease (AD) and normal pressure hydrocephalus (NPH) is complicated by overlapping clinical manifestations. This challenges accurate clinical diagnosis and highlights the need for molecular level investigations to understand underlying pathologies. There have been few proteomic investigations into NPH, which were limited by low sample sizes and limited analytical depth. Here we applied machine learning to investigate the distinct and overlapping proteomic signatures associated with AD and NPH, compared to that of cognitively unimpaired controls (CU).MethodBanked cerebrospinal fluid (CSF) samples were obtained from diagnostic lumbar punctures at an outpatient neurology clinic. Participants were classified based on clinical presentation, improvement after a high‐volume LP, and CSF amyloid‐b status as CU (N = 53), AD (N = 158), or NPH (N = 56). Proteomic quantification was done using data‐independent acquisition mass spectrometry. Defining molecular signatures between classes was investigated with random forest models in two‐way fashion and top features were assessed using Gini importance. Additionally, to determine functional changes, we examined enriched and depleted pathways with gene‐set enrichment analysis (GSEA) using fold‐change data between classes.ResultRandom forest models obtained high classification accuracy (>75%). Comparison of the top 30 proteins of each model indicated four proteins, MASP1, NRXN2, VCAN and LTBP2 as defining features in NPH compared to both CU and AD. Six proteins, SMOC1, PTPRN2, NPTX2, LUM, APLP1 and GFRA2 were defining features for both AD and NPH compared to CU. One protein, TTR, was a defining feature comparing AD to both CU and NPH. GSEA indicated 91 pathways to be differentially regulated in NPH compared to both CU and AD. Further analysis indicated specific enrichment of complement activation and immune response pathways in NPH. Pathways differentially regulated in AD compared to both CU and NPH included pathways related to glycolysis and metabolic processes.ConclusionOur analyses validate previously suggested mechanisms and provide novel insights into the differential pathologies of AD and NPH. Hereby, we aim to contribute to the development of more refined and early diagnostic tools, facilitating targeted therapeutic approaches for these neurologically challenging disorders.
Examining the relationship between angiopoietin‐2 and cognition in early Alzheimer’s disease
Authors: Song Bing Xin , Beroncal Erika , Andreazza Ana C. , Gallagher Damien , Marzolini Susan , Rapoport Mark J. , Charles Jocelyn , Herrmann Nathan , Oh Paul I. , Kiss Alex , Swardfager Walter , MacIntosh Bradley J. , Rajji Tarek K. , Marotta Giovanni , Siddiqui Mehreen , Vieira Danielle , Kusumo Raphael , Lanctôt Krista L. 
Published: January 2025

AbstractBackgroundAngiopoietin‐2, which regulates endothelial permeability and angiogenesis functions, is emerging as a potential biomarker for early Alzheimer’s disease (AD). Elevated levels of cerebrospinal fluid angiopoietin‐2 concentrations have been found in early AD and may be associated with the breakdown of blood‐brain barrier that can contribute to cognitive decline. There is a lack of research on blood angiopoietin‐2 levels and cognition in early AD. Hence, we examined the association between serum angiopoietin‐2 and global cognition.MethodParticipants were diagnosed with mild cognitive impairment (MCI) due to AD or mild Alzheimer’s dementia using the Diagnostic and Statistical Manual of Mental Disorders 5 criteria for mild or major neurocognitive disorder. Serum angiopoietin‐2 concentrations (ng/mL) were measured and analyzed using enzyme‐linked immunosorbent assays. Global cognition was assessed by the Montreal Cognitive Assessment (MoCA). Linear regression analyses were used to assess the association between angiopoietin‐2 levels and cognition adjusting for covariates selected a priori: age and the presence of cardiovascular risk factors or disease (cardiovascular status). Angiopoietin‐2 levels were log‐transformed prior to analyses due to significant departure from normality in Shapiro‐Wilk tests.ResultOf 30 participants [17 (57%) male, 21 (70%) MCI, 22 (73%) with cardiovascular risk factors or disease], the mean (SD) age was 74.8 (8.7), years of education was 16.3 (2.2), MoCA total score was 21.9 (3.4), and angiopoietin‐2 levels were 4.0 (3.6) ng/mL. Higher angiopoietin‐2 was associated with better cognition [F(3,26) = 6.7, p = .002, R2 = .44]. Controlling for age and cardiovascular status, one log angiopoietin‐2 unit was associated with a 4.2 (SE= 2.0) higher score in global cognition (p = .048, f2 = .10).ConclusionIn this sample, higher serum angiopoietin‐2 concentrations were associated with better global cognition, controlling for age and cardiovascular status. This relationship supports the potential involvement of angiogenesis in cognitive functions and neurodegenerative diseases and suggests the need for further research to explore cardiovascular functions and cognition in early AD.
Dissociable spatial topography of neurodegeneration in Early‐onset and Late‐onset Alzheimer’s Disease: A head‐to‐head comparison of MRI‐derived atrophy measures between the LEADS and ADNI cohorts
Authors: Katsumi Yuta , Touroutoglou Alexandra , Brickhouse Michael , Eckbo Ryan , Joie Renaud La , Eloyan Ani , Nudelman Kelly N. , Foroud Tatiana M. , Dage Jeffrey L. , Carrillo Maria C. , Rabinovici Gil D. , Apostolova Liana G. , Dickerson Bradford C. ,   the LEADS Consortium
Published: January 2025

AbstractBackgroundUnderstanding how early‐onset Alzheimer’s disease (EOAD) differs from typical late‐onset AD (LOAD) is an important goal of AD research that may help increase the sensitivity of unique biomarkers for each phenotype. Building upon prior work based on small samples, here we leveraged two large, well‐characterized natural history study cohorts of AD patients (LEADS and ADNI3) to test the hypothesis that EOAD patients would show more prominent lateral and medial parietal and lateral temporal cortical atrophy sparing the medial temporal lobe (MTL), whereas LOAD patients would show prominent MTL atrophy.MethodWe investigated differences in the spatial topography of cortical atrophy between EOAD and LOAD patients by analyzing structural MRI data collected from 211 patients with sporadic EOAD and 88 cognitively unimpaired (CU) participants from the LEADS cohort as well as 144 patients with LOAD and 365 CU participants from the ADNI3 cohort. MRI data were processed via FreeSurfer v6.0 to estimate cortical thickness for each participant. A direct comparison of cortical thickness was performed between EOAD and LOAD patients based on W‐scores (i.e., Z‐scores adjusted for age and sex relative to CU participants within each cohort) while controlling for MMSE total scores. All patients underwent amyloid PET with 18F‐Florbetaben or 18F‐Florbetapir and amyloid positivity was centrally determined by quantification‐supported visual read.ResultAs expected, a direct comparison of cortical thickness between patients with EOAD and LOAD revealed a double dissociation between AD clinical phenotype and localization of cortical atrophy: EOAD patients showed greater atrophy in widespread cortical areas including the inferior parietal lobule (EOAD marginal mean W‐score ± SEM = ‐1.33±0.08 vs. LOAD = ‐0.52±0.09, p<.001, η2=.097), precuneus (‐1.66±0.09 vs. ‐0.59±0.10, p<.001, η2=.13), and caudal middle frontal gyrus (‐1.65±0.08 vs. ‐0.90±0.10, p<.001, η2=.074), whereas LOAD patients showed greater atrophy in the entorhinal/perirhinal cortex and temporal pole (‐1.00±0.09 vs. ‐1.41±0.11, p<.008, η2=.019).ConclusionThese findings demonstrate a clearly dissociable spatial pattern of neurodegeneration between EOAD and LOAD, supporting our previously developed LOAD and EOAD signatures of cortical atrophy, which underlies the distinct episodic memory and other cognitive characteristics of these AD clinical phenotypes.
Integrative Analysis of Genetic Network and Brain Imaging using Generative Models for Alzheimer’s Disease Prediction
Authors: Lee Dong‐gi , Jung Sang‐Hyuk , Shen Li , Kim Dokyoon 
Published: January 2025

AbstractBackgroundThe structural characteristics of the brain, specifically the decrease of individual gray (or white) matter volumes, provide valuable insights into brain function and cognitive decline, including the development of Alzheimer’s disease (AD). In addition, genetic factors can play a significant role in changes in brain volumes, influencing biological activities and interacting in complex ways. In this study, we aim to investigate the relationship between genetic factors, structural brain volume, and the risk of AD.MethodThe proposed method utilizes a SNP network to capture interactions between genetic variants based on linkage disequilibrium scores. The network represents the complex relationships among genetic factors. Feature propagation, a technique that refines genetic variant information based on graph neural networks (GNNs), is then employed to predict AD risk scores. Meanwhile, brain imaging endophenotypes, following the concept of transcriptome‐wide association studies (TWAS), are used to enhance prediction power and identify significant regions affecting specific diseases. Moreover, a generative artificial intelligence model is applied to produce pseudo‐imaging endophenotypes for samples with only genetic information.ResultThe proposed method was applied to data including (i) both genetic and brain imaging information from two datasets: UK Biobank (UKBB), Alzheimer's Disease Neuroimaging Initiative (ADNI), and (ii) only genetic information from the All of Us research program (AoU). The results were cross‐validated from the two datasets (UKBB and ADNI), and a generative model was applied to AoU. The final prediction results were compared with other models, including the polygenic risk score–continuous shrinkage (PRS‐CS) model. Compared to the other methods, the proposed method demonstrated superior prediction performance and revealed genetic variants and brain regions that impact AD.ConclusionBy integrating genetic information and brain imaging data, this study achieved improved disease risk prediction and a deeper understanding of the phased mechanism of AD associated with genetic variants and brain lesions. Furthermore, the insights gained into the relationship between genetics, brain structure, and diseases are expected to contribute to advancing our knowledge of disease progression and inform future research and clinical strategies for the prevention and management of these conditions.
GEAR 2.0 ‐ Detection of Dementia and Cognitive Impairment
Authors: Hwang Ula 
Published: January 2025

AbstractOlder adults use the emergency department (ED) as an important source of acute medical care, making 20+ million visits annually. Persons living with dementia are twice as likely to use the ED and 1.5 times more likely to have an avoidable visit. When in the ED, they often struggle with the fast‐paced setting and may not be able to give a complete medical history. These challenges, along with other adverse events, put patients with dementia at greater risk for poor outcomes. Yet emergency care for older adults is suboptimal, and care is especially poor for older adults with dementia, even though these adults seek ED‐based care more regularly than matched controls.Three decades of research has shown that dementia is under‐recognized in emergency departments, despite a proliferation of screening tools. Under‐recognition of dementia in the ED leads to under‐recognition in inpatient services, which has broad‐reaching consequences, such as longer hospital stays, lower patient satisfaction, accelerated cognitive declines, and increased health care costs. Better detection of dementia may improve the ED staff’s ability to implement interventions that can reduce the rate of cognitive decline and improve care coordination and patient safety.This session will review the literature of current and past dementia detection approaches and discuss top research questions generated by the patients, care partners and members of the transdisciplinary GEAR Detection Work Group.
A machine learning approach to predict amyloid and tau positivity using clinical features
Authors: Arnold Daniel , Borelli Wyllians Vendramini , Machado Luiza Santos , Rahmouni Nesrine , Therriault Joseph , Servaes Stijn , Stevenson Jenna , Macedo Arthur C. , Schumacher‐Schuh Artur Francisco , Mattjie Christian , Barros Rodrigo C. , de Bastiani Marco Antônio , Lussier Firoza Z , Chamoun Mira , Bezgin Gleb , Benedet Andrea L. , Pascoal Tharick , Rosa‐Neto Pedro , Zimmer Eduardo R. ,   Alzheimer's Disease Neuroimaging Initiative (ADNI)
Published: January 2025

AbstractBackgroundScreen failure due to amyloid negativity is yet a problem in clinical trials for anti‐amyloid drugs. In this context, clinical characteristics of patients presenting with cognitive decline may decrease the screen failure ratio by increasing the odds of selecting individuals with brain amyloid pathology. Herein, we aimed at estimating amyloid and tau positivity in individuals using clinical variables in a machine learning model of prediction.MethodWe selected 1694 participants with amyloid and tau status from ADNI, TRIAD, PPMI databases. Commonly shared clinical features selected between datasets were age, total MOCA scores, clinical diagnosis, sex, education, BMI, heart disease, stroke, hyperlipidemia and diabetes. Amyloid positivity was defined by amyloid‐PET (PIB‐PET, FBB‐PET or AZD4694‐PET) or CSF AB42 and Tau positivity defined by Tau‐PET (MK6240‐PET or AV1451‐PET) or CSF p‐tau181. The dataset was split into training (49%), validation (21%), and testing datasets (30%). A XGBoost model was tuned, and then used to predict the tau status outcome in the testing dataset.ResultA total of 927 men and 767 women were included with mean MOCA 25.7 ± 3.8 scores (Table). The population consisted of 1276 CN, 290 MCI and 128 dementia individuals (1357 A‐T‐ and 337 A+T+). The receiver‐operator characteristic (ROC) analysis showed that the area under the curve (AUC) was 0.86 for discriminating A‐T‐ vs. A⁺T⁺, with 0.82 as sensitivity, specificity as 0.90 and accuracy as 0.88. The top 3 most impactful features were found to be age, diagnosis and MOCA score through a SHAP value analysis (Figure).ConclusionPredicting amyloid and tau positivity with clinically collectible variables may improve selection of individuals for anti‐amyloid trials. A two‐step workflow using this approach may significantly reduce the costs of AD trials. The following studies may incorporate an individualized calculator using clinical variables to estimate amyloid and tau positivity.
Multi‐omics analysis of neuron‐derived small extracellular vesicles to identify novel peripheral biomarker of cognitive impairment in individuals with type 2 diabetes
Authors: Kumar Ashish , Cleary Jacob Alexander , Su Yixin , Singh Sangeeta , Howard Marjorie , Hayden Kathleen M. , Lee Jingyun , Furdui Cristina M , Espeland Mark A. , Ding Jingzhong , Deep Gagan 
Published: January 2025

AbstractBackgroundCentral nervous system (CNS) dysregulated insulin and peripheral hyperinsulinemia has been associated with AD. However, analyzing CNS insulin resistance in living subjects and its implication on cognitive impairment/ AD is difficult to establish due to inaccessibility of brain tissue. In this study we isolated and characterized plasma neuron‐derived small extracellular vesicles (NDE), and adopted multi‐omics approaches to discover novel biomarkers of AD and CNS insulin resistance and suggested their possible association.MethodPlasma samples were obtained from Look AHEAD‐MIND study with 28 cognitively normal (18 female and 10 male) and 28 individuals with probable dementia (11 female and 17 male) based on central adjudication by an expert panel. These individuals had established type 2 diabetes (T2D) and overweight/obesity when enrolled in a randomized controlled clinical trial of a 10‐year behavioral intervention. NDE were enriched from plasma sequentially using CD171 and synaptophysin surface markers by immunoprecipitation and characterized for non‐coding RNAs by small RNA‐sequencing and proteomics by mass spectrometry.ResultThe small RNA‐sequencing analysis identified the expression of over 100 miRNAs in NDE from both groups. The Comparison of differentially expressed miRNAs revealed 42 miRNAs to be downregulated, while 23 miRNAs were upregulated in individuals with dementia. Importantly, all these miRNAs (both up‐ and –downregulated) were found to be over‐represented in AD‐ and insulin signaling‐related pathways. Also, the targets of these miRNAs were associated with pathways like protein phosphorylation, autophagy, cell cycle and nervous system development. Alongside, the mass spectrometry‐based proteomics analysis identified 3 proteins (haptoglobin, complement component C6, and alpha‐2‐macroglobulin) to be significantly downregulated and 1 protein (desmocolin‐1) to be upregulated in the NDE of individuals with dementia. Interestingly, NDE showed upregulated expression of miR‐185‐5p, with concomitant downregulation of one of its potential targets complement protein C6, both known to be associated with AD and insulin signaling.ConclusionThe study demonstrated multi‐omics approach to identify novel plasma NDE biomarkers for dementia pathogenesis and its association with CNS insulin resistance in individuals with T2D.
Retinal mitochondrial Fluorescence‐Lifetime Ophthalmoscopy data in patients with Alzheimer’s Disease
Authors: Shivok Kylee , Affel Elizabeth , Sergott Robert C. 
Published: January 2025

AbstractBackgroundFLIO a novel in vivo reproducible, non‐invasive imaging technology, measures fluorescence lifetime decay in two spectral channels for short‐lived retinal chromophores in two domains: Channel 1 emission wavelength 498‐560 nm corresponding to NADH and FAD/ATP function and Channel 2, 560‐720 nm wavelength corresponding to lipofuscin/lysosomal function. These data reflect the retinal mitochondrial molecular environment. Mitochondrial dysfunction has been recently explored as a cause of decreased synaptic function and cognitive decline in AD. Only very limited data with first generation FLIO technology were previously reported.MethodTwenty‐two individuals [48 eyes], 11 AD patients and 11 controls, underwent FLIO with second generation FLIO manufactured by Heidelberg Engineering. Imaging data were analyzed using SPCImage technology.ResultPatients with AD demonstrated a right shift towards longer wavelength chromophores for both channels 1 and 2. The mean fluorescence decay time of the eye was also extended, leading to much broader wavelength plateaus compared to relative small areas under normal parabolic curve found in controls. In this small cohort, no differences were observed between amnestic and non‐amnestic patients with AD. Mitochondrial changes were more pronounced with longer disease duration and severity.ConclusionFLIO detected changes consistent with changes in the retinal mitochondrial molecular environment for both NADH, FAD, and lipofuscin not otherwise visible with other retinal imaging techniques. FLIO data add support to the hypothesis that mitochondrial dysfunction may contribute to AD neurodegeneration. FLIO may help to resolve the question of whether mitochondrial dysfunction is a primary event for AD and/or a secondary amplifier of the disease. Because it is non‐invasive, painless, reproducible, FLIO has promise as an imaging biomarker to aid in the diagnosis of AD as well as an outcome metric for longitudinal clinical trials.