bluuebunny/crossref_metadata_2025_split huggingface dataset:
Consistent classification of amyloid status by fasted and non‐fasted plasma Aβ42/40 and Amyloid Probability Score
Authors: Bonomi Samuele , Benzinger Tammie L.S. , Xiong Chengjie , Morris John C. , Braunstein Joel B. , Schindler Suzanne E. 
Published: January 2025

AbstractBackgroundIt is unknown whether fasting status affects classification of amyloid status by plasma Aβ42/40.MethodsThe cohort included 50 amyloid PET positive and 50 amyloid PET negative individuals enrolled in studies of memory and aging at the Knight Alzheimer Disease Research Center (ADRC). Included individuals had a non‐fasted plasma sample and an amyloid PET scan performed within 3 months of a fasted plasma sample. Non‐fasted samples were collected throughout the day. CSF was collected at approximately 8 am at the same session as the fasted plasma sample. Plasma Aβ42/40 was measured by an immunoprecipitation‐mass spectrometry (IPMS) assay at C2N Diagnostics. The classification accuracy of amyloid PET and CSF status was evaluated in the fasted and non‐fasted samples by logistic regression. Cut‐offs for amyloid positivity with the highest Youden index (combined sensitivity and specificity) were selected. The correlations of fasted and non‐fasted plasma Aβ42/40 and amyloid probability score (APS, a modeled value incorporating plasma Ab42/40, ApoE proteotype, and age) were evaluated.ResultsParticipants had an average age of 69.3 ± 8.3 years (mean ± standard deviation) and 94 of 100 were cognitively unimpaired. The average interval between collection of fasted and non‐fasted plasma samples was 1.8 ± 0.7 months. Classification accuracy of amyloid status based on fasted or non‐fasted samples (either plasma Aβ42/Aβ40 or APS) was almost identical: for amyloid PET status, ROC AUC 0.88 and 0.89 respectively; for CSF Ab42/40 status, ROC AUC 0.89 and 0.91 respectively. The APS cut‐off for amyloid PET status and CSF status was 16 for both fasted and non‐fasted samples; based on this cut‐off, 8 of 100 samples had discordance between fasted and non‐fasted plasma APS. The Spearman correlation between fasted and non‐fasted plasma for Ab42/40 was ρ=0.74 (0.64‐0.82) and for APS was ρ=0.87 (0.82‐0.91).ConclusionsMeasurements of plasma Aβ42/40 with an IPMS assay and the APS had high and consistent associations with amyloid PET and CSF Aβ42/40 status in fasted and non‐fasted samples. These results suggest that fasting status, and potentially the time of day that a sample is collected, does not significantly affect classification of amyloid status by plasma Aβ42/40 or APS.
Associations between BDNF Val66Met and tau‐PET and episodic memory performance in sporadic AD
Authors: Thomson Diny , Rosenich Emily , Maruff Paul , Lim Yen Ying ,   the Alzheimer’s Disease Neuroimaging Initiative
Published: January 2025

AbstractBackgroundIn the presence of abnormally high amyloid (Aβ+), carriage of the brain‐derived neurotrophic factor (BDNF) Val66Met polymorphism Met allele (Met66) is associated with faster clinical disease progression, greater neuronal loss and faster rate of CSF total‐tau and p‐tau181 compared to matched Val66 Aβ+ homozygotes. Aβ levels are unaffected by carriage of Met66. This suggests reduced neurotrophic support may accelerate Aβ‐related neuronal dysfunction and cognitive decline. We aimed to clarify the role of BDNF Val66Met in moderating region‐specific neurofibrillary tangle (NFT) formation in the brain in sporadic Alzheimer’s disease (AD).MethodData from 26 Aβ+ older adult Met66 carriers and 68 Val66 homozygotes enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were included in the analysis if they had at least one tau‐PET scan, Aβ‐PET scan, and neuropsychological assessment available. Participants were not excluded based on disease stage. ANCOVAs were conducted to determine, cross‐sectionally, whether levels of tau‐PET tracer retention at each Braak stage, episodic memory performance (measured on ADNI’s memory composite), and Aβ levels differed between Met66 carriers and Val66 homozygotes at the time of their first tau‐PET scan. Aβ levels, APOE ε4 status, sex, age, baseline diagnosis, and years of education were included as covariates.ResultAβ+ Met66 carriers showed significantly greater tracer retention in the entorhinal cortex (i.e., Braak 1) compared to Val66 homozygotes (p=.03), with the magnitude of this difference moderate (d=0.39; Table 1). There were no significant differences between Met66 carriers and Val66 homozygotes in tracer retention across all other Braak stages, episodic memory performance or Aβ levels (Table 1).ConclusionThe greater tau‐PET tracer retention in the entorhinal cortex in Met66 carriers may reflect their greater NFT pathology relative to Val66 homozygotes during early stages of AD. This is consistent with previous observations of higher levels of CSF total‐tau and p‐tau181 in Aβ+ Met66 carriers, both in sporadic AD and autosomal dominant AD. Together, these findings support the hypothesis suggesting that loss of neurotrophic support, associated with Met66 carriage, may confer greater vulnerability to Aβ‐related neurodegeneration compared to Val66 homozygosity.
Staging of tau accumulation associated with cognitive decline in AD progression using 18F‐MK‐6240 PET data
Authors: Shafiee Neda , Fonov Vladimir S , Rajabli Reza , Therriault Joseph , Rahmouni Nesrine , Servaes Stijn , Gauthier Serge , Stevenson Jenna , Poltronetti Nina Margherita , Rosa‐Neto Pedro , Collins D Louis 
Published: January 2025

AbstractBackgroundAlzheimer's disease is characterized by the accumulation of amyloid beta and the formation of tau neurofibrillary tangles (NFTs), leading to irreversible neurodegeneration. The formation of NFTs is believed to follow a stereotypical pattern known as Braak stages. Here, using tau‐PET tracer 18F‐MK‐6240 we aim to analyze patterns of Tau accumulation associated with AD‐related cognitive decline and build an in‐vivo, data‐driven staging system based on longitudinal data, using an estimated latent time of disease onset based on cognitive scores to place all subjects on a common timeline.MethodWe used 18F‐MK‐6240 scans from the TRIAD dataset, including 194 cognitively normal, 99 mild cognitive impairment and 77 with Alzheimer’s disease dementia. We used a trajectory model (Kühnel et al. 2021) to align patients based on their longitudinal cognitive scores along a continuous latent disease timeline. ADAS‐cog‐13, CDR‐SB and MMSE were used simultaneously to estimate time‐shifts for each subject. As there were not enough longitudinal time‐points in the TRIAD dataset to directly apply this method, we first applied the method to the full ADNI dataset and then used a nearest‐neighbour technique to impute the disease offset for TRIAD subjects from the closest 38 subjects in the ADNI cohort. (n=38 was found to be optimal through cross‐validation within ADNI.) This supervised imputation model used baseline cognitive test scores (MMSE and CDR‐SB) along with the age of participants to impute their latent disease onset.ResultWe defined 5 2‐year windows on the 10‐year span of the estimated latent disease offset timeline from 4y before onset and up to 6y afterwards. Tau PET scans for subjects within each window were averaged, resulting in 5 average Tau templates, in addition to an initial template generated using amyloid negative normal participants. This staging system depicts the incremental tau accumulation along with the decline in cognition. Medial temporal regions show initial accumulation, starting in transentorhinal and entorhinal cortices, and later stages show full brain involvement.ConclusionUsing purely data‐driven techniques, this method reveals patterns of tau accumulation associated with cognitive decline. These models will help understand the link between Tau and cognitive decline, even for those subjects that don't fit Braak framework.
Performance of a smell identification test vs. the mini‐mental status exam for the detection of dementia and cognitive impairment among ethnically diverse persons with cognitive concerns in primary care
Authors: Luchsinger Jose A. , Valdez Lenfis , Rosario Dahiana , Eimicke Joseph P , Silver Stephanie A , Kong Jian X , Teresi Jeanne , Goldberg Terry E. , Devanand Davangere 
Published: January 2025

AbstractBackgroundEarly detection of dementia and cognitive impairment is recommended for persons 65 years and older during wellness primary care visits. The importance of early detection has increased with the availability of new treatments for early Alzheimer's disease (AD). However, there is no clear approach for early detection in primary care. Odor identification deficits predict AD in epidemiological studies and may be useful for early detection. Our objective was to compare the accuracy of a short odor identification test with a short cognitive screening test for the detection of dementia and cognitive impairment in elderly persons with cognitive concerns.MethodThis was a cross‐sectional study of 600 participants 65 years and older, without known mild cognitive impairment (MCI) or dementia, with cognitive concerns, attending primary care practices in New York City. The odor identification test was the Brief Smell Identification Test (BSIT). The comparator test was the Mini Mental Status Exam II (MMSE). Cognitive diagnoses were made using the National Alzheimer’s Coordinating Center Uniform Data set (NACC‐UDS) version 3 forms with slight modifications by a diagnosis consensus committee. Test performance was compared using Receiver Operating Characteristic analyses.ResultThe mean age of the sample was 72.65 ± 6.31 years, 73.3% were female, 63.3% were Hispanic, 17.5% non‐Hispanic Black, and 27.0% non‐Hispanic White; 23.5% had normal cognition, 27.6% had cognitive impairment‐not mild cognitive impairment (MCI), 31.1% had amnestic MCI, 5.6% had non‐amnestic MCI, and 12% had dementia. The MMSE was superior to the BSIT in detecting dementia (AUC 0.89 vs 0.78, p =0.0007, Figure Panel A) and any cognitive impairment (AUC 0.79 vs 0.63, p <0.0001, Figure Panel B). Combining abnormal scores in the BSIT (< 9) to MMSE (< 24) improved the MMSE’s specificity (0.98 combined vs. 0.92 MMSE alone) and positive predictive value (PPV) in detecting cognitive impairment (0.98 combined vs. 0.95 MMSE alone).ConclusionThe MMSE was superior to the BSIT in detecting dementia and cognitive impairment in primary care but using both tests improved specificity and PPV for identifying persons with subjective complaints needing further cognitive and biomarker evaluation.
Longitudinal changes in cognition in relation to PET biomarkers
Authors: Hartley Sigan L 
Published: January 2025

AbstractBackgroundAdults with Down syndrome (DS) begin accumulating extracellular brain amyloid‐beta (Aβ) plaques in the decade or two preceding symptomatic Alzheimer’s disease (AD). The present study examined the time course of cognitive impairments cross‐sectionally and across two time points (spaced 32 months) in relation to Aβ chronicity (i.e., estimated years to or from reaching Aβ positivity [Aβ+], defined as 18 Centiloids.MethodsAnalyses included 167 participants with DS (M age = 38.91, SD = 8.41) in the Alzheimer Biomarker Consortium – Down Syndrome (ABC‐DS). Participants completed baseline PET scans using [11C]PiB or [18F]florbetapir and were administered the modified Cued Recall Test (mCRT) and Down Syndrome Mental Status Examination (DSMSE). A study partner completed the National Task Group‐Early Detection Screen for Dementia (NTG‐EDSD). A clinical case consensus approach, blind to neuroimaging data, was used to classify participants as cognitively stable, mild cognitive impairment (MCI), dementia, or unable to determine. Methods were repeated 32 months later. A sampled iterative local approximation algorithm modeled Aβ trajectories longitudinally and assigned each participant a chronicity value, representing timeline to Aβ+ (i.e., Aβ+ = 18 Centiloids) or post Aβ+ (Zammit et al., 2023).ResultsMixed model Linear regressions showed significant quadradic associations between Aβ chronicity and cognitive performance, both at baseline (Figure 1) and across the 32 months (Figure 2). Using broken stick regression models, declines in mCRT scores began 3.41 years following Aβ+ in longitudinal models, with a decline of 1.35 (95% confidence interval ‐1.85 to ‐0.89) points per year. On average, participants with MCI were estimated to be 7.40 (SD = 6.68) years post Aβ+ and those with dementia were 12.72 (SD = 5.61) years post Aβ+ 9 (Figure 3).ConclusionWithin‐person memory declines on the mCRT begin at 3 to 4 years following Aβ+ (i.e. Centiloids Aβ+) in DS. After this change point, declines in mCRT were estimated at 1.35 points per year. On average, adults with DS with MCI were 7.4 years post Aβ+ and those with dementia were 12.7 years post Aβ+. Characterizing the timeline of Aβ+ relative to AD symptomology in natural history studies offers estimates for guiding clinical AD trials.
Utility of plasma p‐tau217 as a secondary outcome for clinical trials across AD continuum
Authors: Ferreira Pamela C.L. , Bellaver Bruna , Povala Guilherme , Aguzzoli Cristiano Schaffer , Ferrari‐Souza João Pedro , Leffa Douglas Teixeira , Lussier Firoza Z , Madeiros Marina Scop , Zalzale Hussein , Rohden Francieli , Soares Carolina , Abbas Sarah , Bauer‐Negrini Guilherme , Karim Helmet T. , Hong Chang Hyung , Roh Hyun Woong , Tudorascu Dana , Karikari Thomas K , Snitz Beth E. , Son Sang Joon , Pascoal Tharick A. 
Published: January 2025

AbstractBackgroundBlood‐based biomarkers offer a cost‐effective and simple alternative for clinical use in the context of Alzheimer's disease (AD). It has already been shown that plasma phosphorylated tau at threonine 217 (p‐tau217) is associated with amyloid (Aβ), neurofibrillary tau tangles, and cognitive decline. Longitudinal studies confirmed its ability to track AD progression. Therefore, the aim of this study is to evaluate the potential utility of changes in plasma p‐tau217 as secondary outcome in clinical trials across the AD spectrum.MethodWe included 189 individuals with available plasma p‐tau217 measurements at two time points (24 months follow‐up) across two cohorts:[MYHAT‐NI, population‐based(USA): 51 Cognitively Unimpaired(CU); BICWALZS, memory clinic(South Korea): 135 Cognitively Impaired(CI) individuals], all with baseline Aβ‐PET. Plasma p‐tau217 was quantified using the ALZpath assay. We used linear regression to investigate p‐tau217 association with cognitive decline. Effect size was determined by biomarker mean change divided by the standard deviation. We also estimated the sample size required to detect a 25% drug effect on p‐tau217 reduction with 80% power at a significance level of 0.05.ResultBaseline and longitudinal changes in p‐tau217 were significantly associated with cognitive decline independently of Aβ status in both CU and CI individuals (Table 1). Functionally, individuals classified with mild dementia (CDR‐SB>4.5) showed greater and pronounced annual changes in p‐tau217 compared to the other groups (Figure 1A). Notably, Aβ+ individuals presented a higher effect size for p‐tau217 changes in both CU(0.79) and CI(0.74) groups, compared to the whole population (Figure 2B). The sample size required for a clinical trial including of Aβ+ individuals were 1,276 for CU and 940 for the CI. Cost analysis revealed that in trials recruiting CU Aβ+ individuals targeting tau pathology using p‐tau217 as a secondary outcome was 1.5, 3.7, and 3.9 times more cost‐effective than 18F‐MK‐6240 tau‐PET, 18F‐Flortaucipir tau‐PET, and p‐tau181, respectively (Figure 2). Similarly, in trials with CI Aβ+ participants, p‐tau217 was 1.5 times less expensive than [18F]‐MK‐6240 tau‐PET, and 3.7 times less than [18F]‐Flortaucipir tau‐PET.ConclusionOur results demonstrated that changes in plasma p‐tau217 is associated with cognitive decline across the AD continuum. Furthermore, p‐tau217 shows promise as a secondary outcome in AD clinical trials.
Cortical and subcortical p‐tau density in CTE: Comparison to Alzheimer’s disease
Authors: Sconzo Daniel , Alosco Michael L. , Groh Jenna R. , Tripodis Yorghos , Daneshvar Daniel H. , Nowinski Christopher , Palmisano Joseph N. , Martin Brett , Mez Jesse , Crary John F. , Alvarez Victor E. , Huber Bertrand Russell , McKee Ann C. , Stein Thor D. , Cherry Jonathan D 
Published: January 2025

AbstractBackgroundThe unique lesion of chronic traumatic encephalopathy (CTE) is the perivascular deposition of hyperphosphorylated tau at the depth of the cortical sulci. The distribution and molecular composition of p‐tau is distinct from Alzheimer’s disease (AD), but differential diagnostic challenges remain. Understanding disease differences in regional density of p‐tau will inform differential diagnosis and interpretation of in vivo biomarkers. Here, we compared autopsy‐confirmed CTE and AD across cortical and subcortical measurements of p‐tau density.MethodThe sample included 109 brain donors with stage III/IV CTE, and 109 age (+/‐ 3 years) and sex similar brain donors with autopsy‐confirmed AD. While the original sample was similar, there is missing data across regions due to older cases not having AT8 stains. CTE was neuropathologically diagnosed using published criteria. NIA‐Reagan was used for AD. Neuropathologists used semi‐quantitative rating scales (0=none, 3=severe) to evaluate p‐tau severity in the dorsolateral frontal cortex (DLFC), inferior orbital frontal cortex (IFC), superior temporal cortex (STC), inferior parietal cortex (IPC), CA1, CA2, and CA4, entorhinal cortex (EC), and amygdala. Digital slide scanning of AT8 tissue was done for quantitative assessment of p‐tau density in the DLFC, STC, IPC, CA1, CA2/3, CA4, and calcarine cortex. Regression and analysis of variance models compared disease groups on each outcome, controlling for age.ResultOn the semi‐quantitative rating scales, those with autopsy‐confirmed CTE had greater p‐tau severity in the CA4‐hippocampus compared with AD (OR=4.43, p=0.003); in contrast, AD had greater p‐tau severity in the CA1‐hippocampus (OR=0.07), EC (OR=0.10), and amygdala (OR=0.24) (ps<0.05). There were no differences for the CA2‐hippocampus. Regarding cortical regions, those with AD had greater p‐tau severity on the DLFC, IFC, STC, and IPC (ORs=0.03‐0.16, ps<0.05). A similar pattern was found for the quantitative p‐tau density data: CTE had greater p‐tau density in CA4‐ (marginal mean difference=7.45) and CA2/3 (marginal mean difference=9.86) (ps<0.01) and AD had higher density of p‐tau across the other regions (i.e., DLFC, IPC, STC, calcarine).ConclusionCompared with AD, CA4‐hippocampus was most affected in CTE. AD had greater p‐tau density across cortical regions. Findings inform neuropathological differential diagnosis and interpretation of in vivo biomarkers in CTE and AD.
The Effect of Sex‐Differences on the Relationship Between White Matter Hyperintensity, Cerebrovascular Reactivity, and Fluid Biomarkers
Authors: Bahrani Ahmed A , Jiang Yang , Powell David K , Katsumata Yuriko , Nahvi Azadeh , Lee Tiffany , Gold Brian T , Goldstein Larry B , Wilcock Donna M. , Jicha Gregory A , Nelson Peter T , Norris Christopher M. 
Published: January 2025

AbstractBackgroundAlzheimer’s disease (AD) and vascular cognitive impairment and dementia (VCID) are the predominant types of dementia in older adults, associated with memory loss and cognitive deficits. White matter hyperintensities (WMH) are linked to both AD and VCID. Astrocytes play a crucial role in WM integrity, encompassing functions like neuroinflammation, oxidative stress, and Aβ clearance. Poorly reactive astrocytes could lead to implications, like WMH or vascular damage. This study aims to explore sex‐differences effect on the correlation between fluid biomarkers, WMH, and cerebrovascular reactivity (CVR).MethodTwenty‐seven participants (mean age 76.8±6.4 years, Female=15) preliminary data were collected from UK‐ADRC/MarkVCID cohorts. A correlation test was employed to examine sex‐differences based on the correlation of fluid inflammatory (GFAP, IL6, IL8, IL10), angiogenic (TDP‐43, and PlGF) biomarkers, and Aβ40 and 42, to global and regional CVR and WMH.ResultsWe observed several sex‐differences: the female group showed a significant correlation between WMH at occipital lobe and IL6 (P=0.031), IL10 (P=0.036), and GFAP (P=0.037), while male group only showed a significant correlation between Aβ42 and WMH at the occipital lobe (P=0.039). CVR data of the female group exhibited a correlation at the parietal lobe (right‐hemisphere) and IL8 (P=0.037) and Aβ40 (P=0.038) and between Aβ40 and CVR temporal lobe (right‐hemisphere, P=0.021). The male group showed a significant correlation between IL6 and CVR at the occipital lobe (left‐hemisphere, P=0.012. Generally, the female group showed higher mean values for all biomarkers except for IL10 and PIGF, but only significant at GFAP and TDP43. Additionally, the correlation test adjusted for age and sex showed that TDP‐43 had a significant correlation with WMH in the temporal (P=0.041), occipital (P=0.024), and parietal (P=0.024) lobes, while GFAP displayed a significant correlation only with WMH in the frontal lobe (P=0.013).ConclusionsDespite the small sample size, which warrants expansion in future studies, we observed interesting findings of sex‐differences in specific brain regions in relation to fluid biomarkers. These biomarkers may arise, in part, from reactive astrocytes, commonly found near many brain lesions, including WM pathology. Further studies are needed to gain deeper insight into astrocyte activities in diseases associated with WMH and CVR, like AD.
Machine‐learning analysis of real‐world multimodal data collected autonomously at home detects dementia as precisely as a traditional composite scale
Authors: Rueda‐Delgado Laura M , Buick Alison R , Nolan Hugh , Rowe James B , Murphy Brian 
Published: January 2025

AbstractBackgroundCurrent tools for Alzheimer's disease screening and staging used in clinical research (e.g. ACE‐3, ADAS‐Cog) require substantial face‐to‐face time with trained professionals, and may be affected by subjectivity, "white coat syndrome" and other biases. Alzheimer's symptomology is multi‐factorial and varies day‐to‐day. To enable stratification for precision treatments, more effective composite endpoints are required, with accurate quantification of functional impairment in individual domains. Ideally these would be measured objectively and frequently in real‐world contexts to improve validity, and to reduce clinical burden in trial and care settings.MethodWe present a machine‐learning stratification analysis using data from the Cumulus real‐world multi‐domain neuroassessment platform, in CNS101, a year‐long study designed with a consortium of 10 pharma companies. Participants completed a range of tablet‐based functional tasks with wake EEG, and separately recorded EEG during sleep. This cross‐sectional analysis uses 1111 sessions from repeated sampling with 101 participants (47 patients with mild Alzheimer’s type dementia, and 54 corresponding controls, recruited across 7 sites in the UK), during an early two‐week burst period of autonomous at‐home use. Bagging of decision trees was used to compare the power of different data sources in discriminating dementia from neurotypical states. This algorithm is widely used for its performance across learning tasks (linear and non‐linear), and heterogeneous datasets of differing size, levels of noise, and collinearity. For each set of input data, 100 classifiers were trained and evaluated (10‐fold cross‐validation in each of 10 random partitions), and the mean Area Under the Curve (AUC) of sensitivity/specificity trade‐off was calculated.ResultA full set of 691 multimodal features yielded a classifier with AUC performance of 0.953 and error rate 4.7% – compared to AUC of 0.955 and error rate 4.5% for the set of 13 ADAS‐Cog subscores. While individual modalities/domains all performed above chance, the 50 features from Cumulus cognitive tasks alone had an AUC performance of 0.951.ConclusionPatients using digital technology autonomously in the home can yield data that matches the discriminative power of a traditional clinician‐administered composite scale. This can enable objective precision measurement of disease severity at scale