bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrated Disease Chronicity Modeling to Map Longitudinal Biomarker Relationships in Alzheimer’s Disease
Authors: Robey Seth , Yee Ka Lai , Stone Julie A 
Published: January 2025

AbstractBackgroundA lesson of the recent progress in Alzheimer’s Disease therapy is that biomarker‐driven trials will be crucial to demonstrating efficacy in the clinic. Many studies have demonstrated the potential predictive power of fluid and imaging biomarkers in guiding patient selection1,2 and continued progress of precision medicine approaches will demand development of multi‐dimensional biomarker arrays. However, correlations between candidate biomarkers change non‐linearly with time, requiring methodologies to align biomarkers across a common disease timescale (time from amyloid positivity; TFAP). We present a disease chronicity analysis using latent time joint mixed effects modeling3 (LTJM) which enables prediction of progression from cross‐sectional data.MethodA model of amyloid PET, tau PET, ADAS‐COG13, MMSE, and CDRSB was estimated with training data from amyloid positive subjects in the ADNI database (N=831). Longitudinal profiles for each endpoint were modeled with a 4‐parameter logistic curve. TFAP was estimated for each subject informed jointly by all biomarkers/endpoints. An external validation set consisted of a random sampling of 76 subjects with longitudinal tau PET and cognitive assessments collected within 3 months prior to the first PET scan (‘baseline data’). Longitudinal predictive power was assessed by projecting disease trajectories from subjects in the validation set, given only the observed baseline data and comparing with the observed post‐baseline data.ResultWithout prior guidance, the model correctly estimated the sequencing of clinical diagnoses (CN, MCI, AD) and the timing of tau and clinical progression are consistent with previous empirical studies; acceleration of clinical progression is closely associated with the onset of tau positivity. When applied to baseline data from the validation set, the model accurately captured the data 2‐7 years post‐baseline across biomarkers, covering the span of a typical trial.ConclusionLTJM enables bridging across biomarkers to extract a full progression profile aligned to a common time scale. The model provides unbiased predictions of progression from cross‐sectional data, highlighting its utility for guiding clinical trial patient selection and its potential for refining subject‐level prognoses. Further modeling efforts will incorporate emerging fluid biomarkers and imaging modalities.Ref: 1) Mattsson‐Carlgren, JAMA Neurol. 2023 Dec; and 2) 2023 Apr; 3) Li, Alzheimers Dement. 2018 Aug
Microbiome‐Immune Interplay in Aging Brains: A Pilot Investigation from the MiaGB Cohort
Authors: Prajapati Santosh K , Kumar Vivek , Shukla Rohit , Szekeres Charles , Ma Yonggang , Jain Shalini , Yadav Hariom 
Published: January 2025

AbstractBackgroundThe microbiota‐immune‐brain axis is implicated in Alzheimer’s disease. Alterations in gut microbiota and immune functions in mild cognitive impairment (MCI) are inconsistent and remain to be understood. This study aims to investigate immune cell phenotyping and its link with gut microbial composition associated with cognitive function.MethodIn this pilot study, we used data and samples from MCI (n=6) and cognitively healthy (n=22) older adults (≥60 years of age) from a large, multi‐site, community‐based prospective clinical study called MiaGB (Microbiome in Aging Gut and Brain) consortium. We recorded measures of cognitive function (using the MoCA score), immunophenotyping (using flow cytometry), and stool microbiome (using whole‐genome metagenomics and bioinformatics pipelines). Differences in immune cell populations and the microbiome were found in the two groups and correlated with cognitive function.ResultThe abundance of immune cells such as granulocytes, neutrophils, T‐cells (CD4, CD8, and NK T‐cells), B‐cells, NK cells, and total monocytes were not significantly distinct. However, the levels of neutrophils and classical monocytes increased, while CD8+ T cells decreased in trend in the blood of MCI participants compared to controls. These immune cells were positively correlated with increased detrimental microbial species while negatively correlated with beneficial microbes in the MCI gut. Altered immune and microbial species discriminated MCI patients well from controls and were associated with MoCA scores.ConclusionThis study provides insights into the role of gut microbiota‐immune interactions in the process of cognitive decline in older adults and indicates the potential to be used as biomarkers to diagnose the risk of cognitive decline‐ a debilitating public health problem in older adults.
Plasma biomarker trajectories: Relationship to AD genetic risk factors among cognitively unimpaired individuals
Authors: Pettigrew Corinne , Wang Jiangxia , Soldan Anja , Hohman Timothy J. , Dumitrescu Logan C. , Wang Mei‐Cheng , Albert Marilyn S. , Blennow Kaj , Bittner Tobias , Moghekar Abhay 
Published: January 2025

AbstractBackgroundFew studies have examined how a range of potential modifiers may influence the trajectories of Alzheimer’s disease (AD) blood biomarkers in those who were cognitively unimpaired when first assessed. This study examined potential modifiers of longitudinal changes in plasma biomarkers, including genetic factors (i.e., APOE e4 allele and AD‐polygenic risk scores (PRS), demographics (age and sex), and follow‐up diagnoses (i.e., progression to MCI or dementia) among participants who were cognitively unimpaired at baseline.MethodAnalyses included a subset of BIOCARD Study participants who had all of the relevant measures of interest, including AD‐PRS (N = 133, M baseline age = 64.7y; 7.3 biomarker measures over 8.3 years of follow‐up; Table 1); 19 (14%) had progressed to MCI or dementia over time. These analyses focused on log‐transformed and standardized values of AB42/AB40, p‐tau181, NfL, GFAP, YKL‐40, and sTREM2 in plasma, measured using cobas Elecsys assays in the NeuroToolKit panel (Roche Diagnostics).ResultIn longitudinal mixed‐effects models, NfL, GFAP, YKL‐40, and TREM2 increased over time, whereas AB42/AB40 decreased over time (Table 2). YKL‐40 increases were greater among individuals with higher AD‐PRS and rates of sTREM2 increase were attenuated among APOE e4 carriers. Additionally, baseline AB42/AB40 levels were lower among APOE e4 carriers and baseline sTREM2 levels were lower in individuals with higher AD‐PRS. Older participants had more abnormal baseline biomarker levels, as well as greater rates of change in p‐tau181. Baseline GFAP and sTREM2 levels were higher among females, and rates of change in YKL‐40 were greater among males. In a separate set of models, with AD genetic risk factors covaried, those who progressed to MCI/dementia had more abnormal ptau181 at baseline (Table 3) compared to those who remained cognitively unimpaired, but biomarker rates of change did not differ by follow‐up diagnosis.ConclusionAmong initially cognitively unimpaired individuals, blood biomarkers were differentially related to APOE e4 genetic risk and AD‐PRS. Only YKL‐40 and sTREM2, each markers of neuroinflammation, demonstrated relationships with an AD‐PRS. Additional work is underway to explore whether lower sTREM2 values among cognitively unimpaired participants with higher AD genetic risk reflect reduced microglial activation during preclinical AD.
Lessons Learned from a Global, Multisite Early Detection Program in Primary Care Implementation Program
Authors: MacLeod Tim 
Published: January 2025

AbstractDavos Alzheimer’s Collaborative Healthcare System Preparedness (DAC‐SP) aims to catalyze global healthcare system transformation, providing patients with quicker access to life‐changing innovations and therapies. Utilizing implementation science, the DAC‐SP Early Detection flagship program launched in 2021, engaging seven healthcare systems across six countries (Brazil, Jamaica, Japan, Mexico, Scotland, and the United States). The program's primary aim was to increase the rate of early detection of cognitive impairment by integrating commercially available digital cognitive assessments (DCAs) into primary care and other non‐specialty care settings. Patients with suspected cognitive decline based on the DCA were then offered a blood‐based biomarker (BBM) to determine the likelihood of amyloid presence in addition to customary follow‐up testing.Over the past two years, health system leaders from the early detection program were convened into a Community of Practice (CoP) that met monthly to discuss operational and clinical challenges, collaborate on solutions, and exchange learnings. As part of the CoP meetings, site leaders identified common implementation challenges for DCAs and BBMs in real‐world primary care settings. These challenges span various levels of health systems, including policy (e.g., reimbursement and procurement mechanisms, lack of guidance and consensus on workflow), the outer setting (e.g., availability/capacity of brain health expertise, lab infrastructure, fit with administrative systems), the inner setting (e.g., training and equipment required for blood draws, shipping logistics, administrative support) and understanding clinician knowledge, attitudes and behavior (e.g., level of comfort or experience with cognitive assessments, interpretation and disclosure of BBM results, access to training, task shifting resources, support from peers). Education about BBM specifications, use cases, and integration into clinical routines was essential in understanding the adoptability and scalability of BBMs in primary care settings.
Quantification of tau pathology in the basal forebrain: Associations with age, amyloid, brain structure and cognition
Authors: Chadwick Trevor , Ward Tyler J. , Baker Suzanne L. , Jagust William J. , Landau Susan M. , Harrison Theresa M. ,   the U.S. POINTER Study Group, the Alzheimer's Disease Neuroimaging Initiative
Published: January 2025

AbstractBackgroundTau pathology accumulates early in the basal forebrain (BF) in Alzheimer’s disease (AD). The feasibility of measuring in vivo BF tau is unclear given PET resolution and possible partial volume effects of off‐target signal (OTS) which varies by tracer.MethodWe compared measurements of tau in cognitively unimpaired older adults with either an FTP or MK6240 scan: 93 FTP scans from the Berkeley Aging Cohort Study (BACS), 424 FTP scans from ADNI (N=517 FTP scans; 72.5±7.1 years, 61% F) and 888 MK6240 scans (68.4±5.2 years, 62% F) from the U.S. POINTER imaging study were processed identically to derive SUVRs in native space FreeSurfer regions and template space BF. Using linear regression we explored associations between tau‐PET ROIs across the brain focusing on the temporal lobe (entorhinal cortex [ERC], temporal MetaROI), BF (Ch1‐3, Ch4) and common regions of OTS (e.g., pallidum, thalamus). We examined relationships between tau in these ROIs and age, Aβ, hippocampal volume, BF volume, and cognition.ResultThe MK6240 cohort was significantly younger, less educated, more racially diverse, and had higher hippocampal and BF Ch1‐3 volume (Table. 1). For both tracers, BF and temporal lobe ROI SUVRs were positively correlated with Aβ, while OTS ROI SUVRs were not related to Aβ (Figure 1). In the BF, the effect size (Cohen’s d) of t‐tests comparing tau SUVR by Aβ status was higher using MK6240 compared to FTP. BF and ERC SUVR positively correlated with age and negatively correlated with hippocampal volume and episodic memory (Figure 2). BF Ch4 SUVR negatively correlated with BF Ch4 volume in both cohorts.ConclusionEvidence that BF tau, along with temporal lobe tau, correlates with Aβ uptake suggests that AD‐relevant tau pathology can be measured in vivo in the BF. In addition, BF tau and temporal tau showed similar relationships to other key variables including age, brain structure and cognition with both tracers. MK6240 slightly better differentiated Aβ positive and negative subjects in BF SUVR measurements compared to FTP. Future research will investigate if BF tau is a critical early marker of AD progression.
DeepSUVR: Using temporal constraints to improve SUVR and Centiloid quantification
Authors: Bourgeat Pierrick , Fripp Jurgen , Gillman Ashley G , Cox Timothy , Goyal Manu S. , Tosun Duygu , LaMontagne Pamela , Benzinger Tammie L.S. , Weiner Michael S. W. , Villemagne Victor L , Masters Colin L , Rowe Christopher C. , Dore Vincent ,   for the Alzheimer’s Disease Neuroimaging Initiative, OASIS3, and the AIBL research group
Published: January 2025

AbstractBackgroundPET quantification using the Standardised Uptake Value Ratio (SUVR) relies on the availability of a robust reference region. Intrinsic noise, spill in, and specific binding in the reference region can impact the reliability of the resulting SUVR. We evaluate a novel deep learning method trained on longitudinal data that penalises unexpected temporal changes and learns a SUVR correction factor that compensates for any noise or bias in the reference region, resulting in an improved quantification.Method3098 participants with 2+ visits in AIBL/ADNI/OASIS (8826 scans) had their Amyloid PET images spatially normalised and quantified using the Centiloid SPM pipeline. A deep learning network (DeepSUVR) was trained to predict a SUVR correction factor (CF) for each spatially normalised image. For each iteration, the prediction was run on 2 random timepoints from the same participant, resulting in 2 Adjusted Centiloids computed using SUVR*CF and each tracer’s standard Centiloid transform. A loss function was defined to penalise unexpected temporal changes: Centiloid decreasing over time, Centiloid deviating from the curve defining the change in Centiloid/Year given the mean Centiloid. The loss also included a penalty for over‐correction (Slope of the linear regression between original and adjusted Centiloid should be 1). The model was trained using a subset of ADNI/AIBL (2037 participants) and evaluated in terms of longitudinal consistency on the remaining ADNI/AIBL participants and on OASIS data. The correlations between head‐to‐head tracers in the OASIS and GAAIN calibration datasets were also computed.ResultDeepSUVR increased the correlation between each pair of 11C‐PIB/18F‐Tracers in the GAAIN calibration dataset (Figure 1) and between pairs of 11C‐PIB/18F‐Florbetapir in OASIS (from R2=0.86 to R2=0.92) (Figure 2). Using DeepSUVR, the correlation between Centiloid/Year vs mean Centiloid increased from ρ=0.39 to ρ=0.48 in AIBL/ADNI and ρ=0.47 to ρ=0.51 in OASIS (Figure 3).ConclusionWe have proposed a novel deep learning technique to correct SUVR estimation based on noise in longitudinal trends. The longitudinal constraint led to higher agreement between tracers in the head‐to‐head GAAIN/OASIS datasets and higher longitudinal consistency. Importantly, while the model was trained using pairs of images, the prediction is run on single images. Future work will evaluate this approach on tau tracers.
Plasma metabolites associated with plasma P‐tau181 in preclinical Alzheimer's disease
Authors: Sharmin Tahmida , Doecke James D. , Chatterjee Pratishtha , Huynh Kevin , Heng Benjamin , Eslick Shaun , Garg Manohar , Martins Ralph N 
Published: January 2025

AbstractBackgroundAlzheimer's disease (AD) pathogenesis is not restricted to amyloid‐beta, Aβ, and tau pathologies but involves dysregulation in diverse cellular and molecular processes. Numerous metabolomic studies revealed plasma metabolite alterations in AD individuals compared to healthy controls. Nevertheless, plasma P‐tau181, an established biomarker for AD diagnosis and prognosis, has been described to reflect initial multiple cortical region Aβ deposition in cognitively intact adults. The current study aims to identify plasma metabolites associated with plasma P‐tau181 at the preclinical stage and better understand the associated biochemical mechanisms for AD pathogenesis.MethodIn the current study, 100 older adults with no objective cognitive impairment, MoCA and MMSE ≥ 26, from the Kerr Anglican Retirement Village Initiative in Ageing Health (KARVIAH) cohort, comprising 65 CI Aβ‐ (cognitively intact normal brain Aβ) and 35 CI Aβ+ (cognitively intact higher brain Aβ) individuals, were assessed for plasma P‐tau181, via ultra‐sensitive Quanterix Simoa technology, and plasma metabolites, via mass spectrometry‐based BIOCRATES kit, and then investigated for associations, both before and after adjusting for confounding variables, in the study groups. Additionally, P‐tau181‐associated plasma metabolites were evaluated using the receiver operating characteristic (ROC) curves for the potential to classify brain Aβ status.ResultIn the entire cohort, significant positive associations of plasma metabolites, including acylcarnitines, amino acid citrulline, and three biogenic amines (creatinine, kynurenine and SDMA), were observed with P‐tau181 and similar associations, except for kynurenine, were detected in CI Aβ‐. In contrast, in CI Aβ+, only acylcarnitine, AC(10:3), was found to have a positive association with P‐tau181 and further, upon including AC(10:3), the AUC for P‐tau181 (AUC=70.9%) potentially outperformed (AUC=76.2%), which additionally topped to 83.9% when combined with a base model (Abstract Figure).ConclusionThese findings suggest that the higher the plasma P‐tau181, the higher the medium chain acylcarnitine, AC(10:3), in plasma in cognitively intact older adults at risk for AD, indicating a link between early Aβ pathology and fatty acid oxidation mediated energy metabolism pathway. Additionally, associated metabolite strengthens the significance of P‐tau181 in classifying brain Aβ status in cognitively intact older adults. Therefore, plasma P‐tau181‐associated plasma metabolite may serve as potential predictive marker for preclinical AD pathogenesis.
Neuroinflammation and synaptic markers relate to trajectories of core biomarkers in Alzheimer’s Disease continuum
Authors: Ersoezlue Ersin , Hellmann‐Regen Julian ,   Alzheimer’s Disease Neuroimaging Initiative
Published: January 2025

AbstractBackgroundMechanisms of cellular senescence affecting neuroimmune responses, homeostasis, and synaptic functioning associate with core Alzheimer’s disease (AD) biomarkers, i.e., pathological aggregation of β‐amyloid (Aβ) and tau protein, while their longitudinal associations remain unclear.MethodThe study cohort included participants from the Alzheimer’s Disease Neuroimaging Initiative study, which consisted of n=65 healthy controls (HC, mean years of every follow‐up visit 4±3), n=41 suspected non‐AD pathology (SNAP, mean years of every follow‐up visit 4±3), and n=198 AD continuum (ADC, mean years of every follow‐up visit 3±3) who underwent lumbar puncture at the baseline and available data for multiplex based immunoassay panel for the a priori selected analytes in cerebrospinal fluid (CSF). We derived adjusted annual change rates as slopes from separately conducted linear mixed models with Aβ42 or phosphorylated‐tau181 (p‐Tau) dependent variables, including age, sex, APOE‐ε4 status and year from baseline.We tested the correlations between annual change rates and nine CSF analytes (listed by category in Figure 1). Significance level was defined as alpha=0.05, and the Bonferroni‐correction method was used for multiple testing. We also explored the associations of an incident abnormality of p‐Tau/Aβ42‐ratio with CSF markers using Cox regression models and subsequently tested interactions.ResultThe demographic, genetic, clinical and biomarker levels are presented in Table 1 for each study group, showing that only FABP‐3 levels were altered in ADC compared to HC and SNAP. In both HC and ADC groups, markers for glia and cell adhesion were negatively correlated with annual change of Aβ42 and positively with p‐Tau, respectively (Figure 1A‐C). Specifically in ADC, higher FABP‐3 was associated with an increasing trend in p‐Tau, while FRTN with a decline in Aβ42 (Figure 1C). In the exploratory cox regressions, higher FABP‐3 levels were associated with conversion to abnormal p‐Tau/Aβ42‐ratio status over the up to ∼15 years follow‐up period (Figure 2).ConclusionOur results suggests that CSF biomarkers based on cell adhesion, glial activity and synaptic function can contribute to a more precise identification of individuals at‐risk for AD and prediction of trajectories of established AD biomarkers. Moreover, identifying related biological stages of AD can guide the development of preventive approaches targeting these pathways.
Plasma GFAP and pTau‐181 Moderate the Relationship between Cognition and White Matter Hyperintensities in a Southeast Asian Non‐Demented‐at‐Risk Cohort
Authors: Sandhu Gurveen Kaur , Vipin Ashwati , Zailan Fatin Zahra , Tanoto Pricilia , Lee Faith Phemie Hui En , Soo See Ann , Ghildiyal Smriti , Liew Shan Yao , Tan Isabelle Yu Zhen , Leow Yi Jin , Kumar Dilip , Azam Mohammed Adnan , Chong Wayne Freeman , Kandiah Nagaendran 
Published: January 2025

AbstractBackgroundAstrocyte reactivity marked by elevations in Glial Fibrillary Acidic Protein(GFAP), was found to increase Phosphorylated Tau181(pTau181) induced neurodegenerative effects in Alzheimer’s Disease (Bellaver et al., 2023). Nevertheless, the effect of the GFAP‐ pTau181 axis in vascular/mixed dementias has yet to be completely elucidated. In this study, we investigated the effect of GFAP‐pTau181 on cognition in a Southeast Asian cohort having high burden of cerebrovascular disease.MethodsA cross‐sectional study was conducted in 190 non‐demented but at risk, Southeast Asian community participants recruited into the Biomarker and Cognition Study. Neuropsychological Assessments of global cognition [Visual Cognitive Assessment Test(VCAT)] and processing speed were measured (Kumar, 2018; Nagaendran et al., 2016; Tian et al., 2022; Wiseman et al., 2018). The burden of white matter hyperintensities (WMH), a surrogate measure for cerebral small vessel disease was quantified using the automated lesion growth algorithm (Lesion Segmentation Tool) (Vipin et al., 2021). Plasma biomarkers (GFAP, Aβ40, Aβ42, pTau181) were quantified using the Single Molecule Array(Simoa) platform (Wilson et al., 2016). A partial correlation analysis was used to demonstrate associations between variables. A moderation analysis was conducted to determine if pTau/GFAP influenced the relationship between cognition and WMH, via linear regression analysis with the inclusion of interaction terms (WMH*pTau/GFAP).ResultsFindings indicate that, WMH volume positively correlated to GFAP and pTau181(Figure 1A and 1B). Information processing speed and VCAT negatively correlated to WMH volume. GFAP negatively correlated to Aβ42/Aβ40 ratio (Figure 2A). pTau181 demonstrated a negative correlative trend with Aβ42/Aβ40 ratio (Figure 2B) and a positive correlative trend with GFAP (Figure 2C). Moderation analysis revealed that GFAP and pTau181 independently influenced the relationship between cognitive measures and WMH volume, such that increasing WMH volume and GFAP/pTau181 levels related to deteriorating processing speed and VCAT scores (Figure 3A and 3B).ConclusionGFAP, a neuroinflammatory measure moderates the relationship between WMH and cognition and may be a useful biomarker for the detection and prognostication of cognitive impairment among Southeast Asians. The pathobiology could be a result of early upstream astrocyte mediated modulation of Blood‐Brain Barrier permeability and resultant perfusion dynamics. pTau181 facilitated effects are likely downstream events of GFAP activation. Further mechanistic validation in a larger longitudinal study is underway.