bluuebunny/crossref_metadata_2025_split huggingface dataset:
Relationship of Mito‐Nuclear Genetics to FDG‐PET Alzheimer’s Disease Biomarkers in Aging
Authors: Honea Robyn A , Vidoni Eric D , Lepping Rebecca J , Morris Jill K , Wilkins Heather M , Burns Jeffrey M , Swerdlow Russell H 
Published: January 2025

AbstractBackgroundA growing amount of data has implicated the various roles of nuclear genes involved in mitochondrial function, and mitochondrial genes, on risk for Alzheimer’s disease (AD) and AD neuroimaging biomarkers. To date, no studies have investigated the relationship of mitochondrial haplogroups or the APOE and TOMM40 genes on brain glucose metabolism, a sensitive early marker of metabolic decline and possible mitochondrial dysfunction in AD.MethodWe analyzed regional standard uptake value ratio (SUVR) differences in 18F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) using SPM12 and CAT12 software between nondemented (ND, n=69), mild cognitively impaired (MCI, n=19) and AD (n=18) groups in a sample of individuals from the University of Kansas Alzheimer’s Disease Research Center Cohort, controlling for age, sex, and education. We then tested whether genetic variation in APOE (ε4‐carriers vs. non‐carriers), TOMM40 rs2075650 (‘650 G‐allele carriers vs. A‐Allele) or mitochondrial haplogroup significantly influenced regional differences in glucose metabolism in nondemented individuals, as they made up the bulk of our sample of individuals with these scans.ResultWe found that, as expected, individuals with AD had significantly decreased glucose metabolism across all regions except the motor cortex and the cerebellum compared to ND individuals (Figure 1). Our results across various gene groups, controlling for age, sex, education, and with and without APOE ε4 showed that there were no significant differences in FDG‐PET SUVR between ND TOMM40 ‘650 G‐allele carriers, or mitochondrial haplogroups (H, JT, and UK groups combined), however APOE ε4‐carriers had significantly lower glucose metabolism in the anterior cingulate, superior medial frontal, posterior cingulate, medial frontal, right middle frontal, and bilateral superior frontal cortices than ε4‐non carriers (Figure 2). There were no significant effects of APOE ε4 on glucose metabolism in limbic regions.ConclusionThe APOE ε4 allele may have more impact on FDG‐PET in default mode regions (posterior and anterior cingulate, medial frontal cortex) than TOMM40 '650, a larger study in cognitively healthy individuals will be needed to confirm these results.
PreSiBOGNN: Multi‐Modal Graph Neural Network for Prediction of Cognitive Improvement in Medication Usage in UK Biobank
Authors: Priyadarshi Dhawal , Huang Heng , Sahelijo Nathan J , Shirkavand Reza , Jun Gyungah R 
Published: January 2025

AbstractBackgroundThe prohibitive costs of drug development for Alzheimer’s Disease (AD) emphasize the need for alternative in silico drug repositioning strategies. Graph learning algorithms, capable of learning intrinsic features from complex network structures, can leverage existing databases of biological interactions to improve predictions in drug efficacy. We developed a novel machine learning framework, the PreSiBOGNN, that integrates muti‐modal information to predict cognitive improvement at the subject level for precision medicine in AD.MethodThe graph neural network framework integrates four layers of input data including transcriptome, proteome, drug, and subject to connect to bipartite graphs in the United Kingdom Biobank (UKBB). Medication usage, clinical, and GWAS data were downloaded for 48187 subjects with first and second cognitive exams from the UKBB. The protein and transcriptome layers were constructed using the String database and gene coexpression networks generated from single nuclei RNA data (Sahelijo et al. 2022). Layers were connected by binary bipartite graphs constructed using drug information from the UniProt and DrugCentral databases. Sequential Graph Attention Networks convoluted embedded features generated by each layer in a hierarchical order: 1. gene‐gene, 2. gene‐protein, 3. protein‐protein, 4. protein‐drug, 5. drug‐subj. Feature embeddings were decoded using a multilayer perceptron to predict cognitive improvement between the first and second cognitive exams. Two models compared methods of data aggregation. The first model follows a strict hierarchy, aggregating inter‐layer data in a single direction (gene > protein > drug > subj). The second model allows for fluid message passing between inter‐layers. We used 60% of the UKBB subjects for training and 30% for validation. We assessed the model with the greatest training accuracy using the remaining 10% of subjects.ResultWe observed that the performance of the strict message‐passing model attained validation and test accuracy of 57.7% and 51.2%, respectively. Performance of the fluid message passing model improved prediction accuracy with 61.3% and 58.1% in the test set.ConclusionOur investigation suggests the feasibility of the PreSiBOGNN framework to infer cognitive improvement of existing drugs by integrating medication, multi‐omics, and clinical data. Future work will focus on model optimizations and the integration of additional modalities including compound‐specific fingerprint data.
Using plasma biomarkers to distinguish TDP43 associated pathologies
Authors: Foley Joseph P , Zacco Anna , Towle‐Miller Lorin , Ong Rachel M Y , Ganji Gopinath 
Published: January 2025

AbstractBackgroundMislocalization and aggregation of TAR DNA‐binding protein (TDP43) leads to loss of function and pathogenesis in 40‐45% Frontotemporal dementia (FTD) and >95% amyotrophic lateral sclerosis (ALS) patients. While TDP43 pathology has been studied in post‐mortem tissues, it has been challenging to evaluate pathology using reliable fluid biomarkers with clinical utility. We examined levels of plasma biomarkers, neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), phosphorylated Tau (pTau181) and total Tau (Tau), and followed up on previous results1 showing lower levels of GFAP:NfL in differentiating FTD‐TDP from FTD‐Tau subpopulations.Method40 ALS and 32 non‐disease age‐matched control plasma samples were procured from Northeast ALS Consortium (NEALS) biorepository and PrecisionMed (Carlsbad, CA, USA). Quanterix digital immunoassays, Neuro 2‐Plex B assay, SIMOA pTau‐181 and SIMOA Tau Advantage kits, were used to determine concentrations of NfL, GFAP, pTau181 and Tau, respectively, on the SR‐X platform. Ratios, correlations and statistical analyses (unpaired t test with Welch’s correction or one‐way ANOVA with Dunnett’s test) were performed across all plasma biomarkers analyzed. Cutoffs from prior studies1 were examined to classify disease vs control samples. Furthermore, the ALLFTD dataset is being analyzed to evaluate relationships between some of these biomarkers and relevant clinical covariates in subgroups of interest.ResultPlasma NfL and GFAP levels were significantly (p<0.0001) elevated in ALS samples compared to non‐disease controls. NfL was elevated (∼7x) relative to GFAP (∼1.8x) in cases versus controls. Furthermore, GFAP:NFL ratio showed a notable separation with lower values being significantly (p<0.0001) associated with disease samples (mean=3.4) versus controls (mean=9.7), as previously reported1. Similarly, higher (∼1.8x) pTau181 and lower (∼0.7x) Tau levels were significantly (p<0.05) observed in ALS versus control samples and higher (mean=20.2) pTau181:Tau ratios achieved a markedly significant (p<0.0001) differentiation of ALS from controls. Interestingly, 80% of ALS plasma samples showed a consistent correlation of higher pTau181:Tau with lower GFAP:NFL ratios while the control samples showed the exact opposite result.ConclusionPlasma biomarkers measured as GFAP:NfL and/or pTau181:Tau ratios have the potential to distinguish TDP43 associated pathologies such as ALS and FTD‐TDP.1Cousins et al., JAMA Neurol. 2022 Nov 1;79(11):1155‐1164
GDF15 as a Prognostic Biomarker of Cognitive Decline in Frontotemporal Dementia
Authors: Chen Coty , Paolillo Emily W. , Saloner Rowan , VandeBunte Anna M. , Cadwallader Claire J. , Lee Shannon Y. , Lago Argentina Lario , Gendron Tania F , Petrucelli Leonard , Webb Julia D , Staffaroni Adam M. , Heuer Hilary W. , Boeve Brad , Forsberg Leah K. , Rosen Howard J. , Rojas Julio C. , Kramer Joel H. , Boxer Adam L. , Casaletto Kaitlin B. ,   ALLFTD Investigators
Published: January 2025

AbstractBackgroundGrowth Differentiation Factor 15 (GDF15) is a TGF‐beta superfamily protein upregulated during immune‐mediated stress responses. Peripheral GDF15 is linked to adverse aging across organ systems including the brain. Immune dysregulation is implicated in frontotemporal dementia (FTD) pathogenesis. We examined how plasma GDF15 levels associated with longitudinal cognitive and neurofilament light chain (NfL) trajectories in adults with FTD and controls. Furthermore, we tested the utility of GDF15 as a prognostic biomarker against common inflammatory proteins (CRP and IL6).Method231 adults with FTD (24.35% sporadic FTD syndromes; 19.19% MAPT; 19.56% GRN; 22.14% C9orf72 pathogenic carriers) and 40 normal controls from the ALLFTD Consortium completed longitudinal neuropsychological testing and a blood draw with plasma analyzed on SomaScan V4.0 (baseline only: GDF15, CRP, and IL6) and Simoa (longitudinal: NfL). CRP and IL6 were selected as control markers to test the specificity of GDF15. Linear mixed effects models separately examined longitudinal cognition (global, executive functioning, memory, and language z‐scores) and plasma NfL levels as a function of baseline plasma GDF15, time, and their interaction, controlling for baseline age, sex, and education. Parallel models were conducted for CRP and IL6.ResultIn the whole cohort, higher baseline plasma GDF15 levels associated with steeper global cognitive decline (b = ‐3.91, p = 0.0092) and NfL accumulation (b = 3.88, p = 0.00088) over time. When examining cognitive domains, we found GDF15 only associated with worse executive functioning trajectories (b = ‐3.19, p = 0.0021). In contrast, baseline levels of CRP and IL6 did not display statistically significant relationships with cognitive or NfL trajectories. Prognostic effects of GDF15 remained statistically significant when restricting analyses to those with FTD pathogenic variants (b = ‐2.98, p = 0.0054 [global cognition], b = 2.69, p = 0.0098 [NfL]).ConclusionAdults with FTD and higher baseline peripheral GDF15 levels exhibit accelerated cognitive decline and neurodegeneration. Our results support GDF15 as a potential prognosticator of adverse clinical outcomes in FTD and highlight the relevance of GDF15‐specific pathways in FTD progression. Plasma GDF15 may provide utility over commonly examined inflammatory proteins and further work to explore its clinical value is indicated.
Quantitative Systems Pharmacology Model for Therapies Targeting Aβ and Tau Pathologies in Alzheimer’s Disease
Authors: Lin Lin , Minucci Sarah , DiBartolo Sarah , Grant Joshuaine , Muralidharan Kumar Kandadi , Hutchison Matthew , Collins Jessica , Edwards Amanda , Hering Heike , Bussière Thierry , Graham Danielle , Hua Fei , Roberts John 
Published: January 2025

AbstractBackgroundAlzheimer’s disease (AD) is a progressive neurodegenerative disease characterized by the formation of amyloid‐beta (Aβ) plaques and neurofibrillary tangles (NFTs) composed of tau aggregates. Research in animal models has generated hypotheses on the underlying mechanisms of the interaction between Aβ and tau pathology. In support of this interaction, results from clinical trials have shown that treatment with anti‐Aβ monoclonal antibodies (mAbs) affects tau pathology. Meanwhile, preliminary results from a phase 1b study showed that BIIB080, a tau‐targeting antisense oligonucleotide (ASO), reduced tau biomarkers in early AD patients. Building upon our previous work of a quantitative systems pharmacology (QSP) model for anti‐Aβ mAbs, we have developed a more comprehensive model incorporating the interplay between Aβ and tau pathways and therapies targeting Aβ and tau.MethodA QSP model was developed that accounts for changes in Aβ and tau biomarkers in the progression of AD. It includes the production, aggregation, and inter‐compartment transport of both Aβ and tau species, as well as changes in phospho‐tau species and spreading of aggregated tau pathology. The model captures the interaction between Aβ and tau pathology by Aβ plaque‐dependent tau hyperphosphorylation that drives NFT formation in Braak region I/II and tau pathology spreading to Braak regions III/IV and V/VI. Furthermore, the model incorporates mechanisms of action for anti‐Aβ mAbs, including aducanumab, lecanemab, donanemab and gantenerumab, and the tau‐targeting ASO, BIIB080. The model was calibrated using Aβ and tau biomarker data (Aβ42/Aβ40, t‐tau and p‐tau181 in CSF and plasma, amyloid and tau PET) from natural history studies as well as pharmacokinetic (PK) data and biomarker response data from the clinical trials with these therapies.ResultModel simulations were in good agreement to the available clinical data. The calibrated model was able to match observed drug PK and Aβ and tau biomarker data, including amyloid re‐accumulation after depletion. In addition, the model was able to reasonably capture the effects of anti‐Aβ mAbs on tau pathology.ConclusionA QSP model was developed to integrate therapies targeting Aβ and tau pathologies in AD. The model can be utilized to explore dosing regimens to support the development of combination therapy.
Lower slow wave sleep and rapid eye‐movement sleep are associated with brain atrophy of AD‐vulnerable regions
Authors: Cho Gawon , Mecca Adam P , Buxton Orfeu M. , Liu Xiao , Miner Brienne 
Published: January 2025

AbstractBackgroundSleep deficiency is associated with an increased risk of Alzheimer’s disease (AD), warranting research on underlying mechanisms. This study examined the association of sleep architecture with anatomical features frequently observed in AD: (1) atrophy of cuneus, hippocampus, entorhinal, inferior parietal, parahippocampal, and precuneus regions (henceforth referred to as “AD‐vulnerable regions”) and (2) the presence of cerebral microbleeds.MethodIn 271 participants of the Atherosclerosis Risk in the Communities Study, we examined the prospective association of baseline sleep architecture with anatomical features of the brain identified on MRI conducted ∼17 years later. Sleep architecture was quantified as the proportion of time spent in slow wave sleep (SWS), proportion of time spent in rapid eye‐movement sleep (REM), and the number of arousals per hour of total sleep time using polysomnography. Outcomes included (1) volumetric measurements of each AD‐vulnerable region and (2) the presence of any cerebral microbleeds (CMBs) and that of lobar CMBs, which are more specifically associated with AD. We analyzed the association of each sleep predictor with each brain outcome, adjusting for demographics, baseline cognitive function, APOE genotype, intracranial volume, other covariates, and false discovery rate (FDR; family‐wise p<.05).ResultHaving less SWS was associated with smaller volumes of the inferior parietal region (β=‐41.56 mm3 per percentage point (PP) [95%CI=‐73.97, ‐9.15]) and cuneus (β=‐11.56 mm3 per PP [‐20.51,‐2.62]) after adjustments for covariates. Having less REM was associated with smaller volumes of the inferior parietal region (β=‐73.13 mm3 per PP [‐126.95, ‐19.30]) and precuneus (β=‐32.46 mm3 per PP [‐64.48,‐0.44]). After FDR adjustments, lower SWS and REM, respectively, were associated with lower volumes of the inferior parietal region only. Arousal index was not associated with the volumes of AD‐vulnerable regions. None of the sleep architecture variables were associated with the presence of CMBs or lobar CMBs.ConclusionSleep deficiency is associated with the atrophy of the inferior parietal region, which is often observed in early AD. Sleep architecture may be a modifiable risk factor for AD pathogenesis.
Clinical effects of amyloid positivity upon positron emission tomography in an Asian cohort with Alzheimer’s disease and other dementias
Authors: Chun Min Young , Jang Hyemin , Yun Jihwan , Kim Jun Pyo , Kang Sung Hoon , Weiner Michael S. W. , Kim Hee Jin , Na Duk L , Hong Chang‐Hyung , Roh Hyung Woong , Lee Tae‐Kyeong , Lee Eek‐Sung , LEE EUN HYE , Shin Daeun , Ham Hongki , Gu Yuna , Kim Yeshin , Kim Chi‐Hun , Woo Sook‐Young , Seo Sang Won 
Published: January 2025

AbstractBackgroundEthnic differences in amyloid‐β (Aβ) characteristics and cognitive trajectories should be considered, when designing Aβ‐targeted therapies. The clinical effects of co‐pathologies of Aβ have not been evaluated extensively across various dementias. We investigated the prevalence of Aβ+ and cognitive trajectories in Koreans and non‐Hispanic whites (NHWs) with Alzheimer’s clinical syndrome (ACS) and the clinical effects of Aβ+ in other dementias.MethodWe included 5,856 Koreans who underwent Aβ positron emission tomography (PET) in South Korea. The participants were categorized into the following groups: ACS, including cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia of the Alzheimer type (DAT); subcortical vascular cognitive impairment (SVCI); and frontotemporal dementia (FTD). Data from 929 NHWs were collected from the Alzheimer’s Disease Neuroimaging Initiative. Mini‐Mental State Examination (MMSE) scores performed at least twice were used to compare cognitive trajectories. We performed multivariable analyses and stabilized inverse probability of treatment weights based on the propensity scores to mitigate imbalances across ethnic groups.ResultThe prevalence of Aβ+ was lower in CU Koreans than in CU NHWs (odds ratio 0·6); however, this pattern was not observed in the MCI and DAT stages. Moreover, Aβ+ Koreans in the CU and MCI stages showed a more rapid decline in MMSE scores than Aβ+ NHWs (CU: B= ‐0·3136, p=0·004; MCI: B= ‐0·3847, p<0·001). Compared to CU, SVCI had a higher prevalence of Aβ+ in the 75–90 years age group (p<0·001). Aβ+ was predictive of a more rapid decrease in MMSE scores in SVCI (B= ‐0·6424, p<0·001) but not in FTD.ConclusionOur findings contribute to understanding Aβ biomarker traits in various dementias in Asians and highlight distinct ethnic differences when compared to NHWs.
Brain Networks Associated with Alzheimer’s Disease Biomarkers
Authors: Mahzarnia Ali , Stout Jacques Andrew , Anderson Robert J , Moon Hae Sol , Lutz Michael W , Staicu Ana‐Maria , Badea Alexandra 
Published: January 2025

AbstractBackgroundWhile we do not yet have the means to detect early Alzheimer’s disease (AD), studying subjects at risk conferred by the presence of the APOE4 allele, can provide useful information before clinical onset. We show that using symmetric bilinear regression with L1 penalty (SBL) of individual (DTI, fMRI) and fused connectomes, we can identify vulnerable regions changing in association with hallmark AD biomarkers measured in cerebrospinal fluid: amyloid beta Aβ42/40, phosphorylated tau (PTAU), and neurofibrillary light (NfL) as a proxy for neurodegeneration.MethodsWe use structural connectomes derived from diffusion‐weighted MRI (DTI) and functional connectomes (fMRI) from 57 subjects, 45 normal controls and 12 cognitively impaired to predict CSF Aβ42/40, PTAU, and NfL to reflect neurodegeneration. To fuse connectomes, we first ranked them and then used the SNFtool. We run SBL for the three types of connectomes (DTI, fMRI, and fusion) and the three biomarkers. We compared the models using a k‐fold cross‐validations scheme and standardized root mean square errors (SRMSE) by dividing RMSE by the standard deviation of the biomarker outcome.ResultsLinear modeling approaches showed that APOE is a significant factor with respect to NfL biomarker measures and that the interaction between APOE and age, APOE and sex, sex and age, were significant for PTAU (Figure 1). The SBL results showed that the best predictions were for NfL, PTAU and Aβ ratio based on DTI connectomes (Table 1.) The common sub‐network across biomarkers via DTI connectomes included the right inferior parietal, inferior temporal, superior parietal and caudal middle frontal, postcentral; and left superior frontal, right precentral, right superior frontal. The common connections across biomarkers via fMRI included the right putamen, left frontal pole and left accumbens, and right transversetemporal gyrus (Figure 2).ConclusionsDTI stood out as the best performer in predicting key biomarkers for AD (NfL, PTAU, Ab ratio). This research enhances our understanding of vulnerable brain networks, including in subjects at risk. Future work will explore strategies for data fusion to preserve common and unique contributions from multiple data sources.
A blood‐based multi‐modal assay for diagnosing Alzheimer's disease
Authors: Modi Souvik , Vergara Hernando M. , Thekkan Shareefa , Chouliaras Leonidas , Malpetti Maura , Swann Peter , Rowe James B , O'Brien John T 
Published: January 2025

AbstractBackgroundCombinations of blood‐based biomarkers have been used to detect Alzheimer’s disease (AD). While these markers provide information about neuropathology, they fail to integrate the cellular dysfunction, such as disease‐associated defects in lysosomal ion homeostasis. To understand cellular dysfunction in AD and its relation to the pathophysiology of the disease, we developed a multi‐modal biomarker diagnostic platform that incorporates lysosomal ionic pH and Ca2+ and plasma levels of Amyloid beta1‐42 (Aβ1‐42), Amyloid beta1‐40 (Aβ1‐40), phosphorylated Tau181 (pTau181), Neurofilament light (NfL) and Glial fibrillary acidic protein (GFAP).MethodThis multi‐modal blood‐based platform incorporates the concentrations of H+ and Ca2+ ions with single lysosome resolution in monocytes measured using DNA nanodevices from 33 participants (20 Controls and 13 AD patients). It also includes a panel of pathological biomarkers, including Aβ1‐40, Aβ1‐42, pTau181, NfL, and GFAP, which are measured using the Simoa platform from plasma across seven different cohorts totalling 323 participants (146 Controls, 92 AD patients, 34 amyloid negative and 51 amyloid positive mild cognitive impairment).ResultThe minimalistic panel of GFAP and pTau181/Aβ1‐42 ratio performs significantly better (area under the curve [AUC] = 85%) in predicting Amyloid PET status than either NfL, GFAP, Aβ1‐42/Aβ1‐40 ratio or pTau181 alone, with AUCs ranging from 68‐77%. The performance of these individual biomarkers or their combinations were further enhanced when lysosomal ionic information was incorporated, differentiating AD subjects from healthy individuals with an accuracy of 88% (Sensitivity 85%; Specificity 95%).ConclusionThe multi‐modal blood platform described here is highly accurate, specific, and sensitive in evaluating AD, representing a promising avenue to explore the connections between cellular biology and the pathological hallmarks of AD, and the diagnosis of this disease.