bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing the correlations between imaging and plasma biomarkers within the AT(N) framework
Authors: Gogola Alexandra , Cohen Ann D , Zeng Xuemei , Lopresti Brian J , Snitz Beth E. , Tudorascu Dana , Minhas Davneet S , Ikonomovic Milos D , Pascoal Tharick A. , Kofler Julia , Matan Cristy , Mason Neale S , Aizenstein Howard J , Mathis Chester , Klunk William E , Zetterberg Henrik , Blennow Kaj , Lopez Oscar L. , Villemagne Victor L , Karikari Thomas K 
Published: January 2025

AbstractBackgroundGiven the increasing usage of plasma biomarkers for Alzheimer’s disease (AD) studies, it is necessary to better understand relationships between plasma biomarker and PET and MR imaging outcomes, particularly within the AT(N) framework.MethodWe evaluated plasma samples from 233 subjects (age 74.05.9y) who underwent 3T MR and both [11C]PiB and [18F]flortaucipir (FTP) PET imaging. SUVR values were calculated for Global PiB, and Meta‐Temporal FTP. An AD signature cortical thickness (CT) composite was derived from MR images using FreeSurfer 5.3, Blood samples were processed and sampled to obtain measures of plasma Aβ42, Aβ40, p‐tau181, p‐tau217, p‐tau231 and neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). Imaging outcomes were split into infra (‐) and supra (+) threshold groups using 1.35 SUVR for Global PiB, 1.18 SUVR for Meta‐Temporal FTP, and 2.7 mm for CT. Plasma Aβ was expressed as Aβ42/Aβ40 ratio while p‐tau181, p‐tau217, p‐tau231, NfL, and GFAP were natural‐log‐transformed to normally distribute the data. Simple regression models between continuous variables of each imaging‐derived measure and all plasma biomarkers were evaluated.ResultFull group regression model R2 and regional p‐values are shown in Table 1, suprathreshold regressions are shown in Table 2. Full Global PiB was significantly associated with all plasma biomarkers while CT was associated with NfL, p‐tau181, and p‐tau231. Meta‐Temporal FTP was associated with GFAP and p‐tau217. Additionally, A+Global PiB was significantly associated with Aβ42/Aβ40, GFAP, and p‐tau217; (N+) CT with NfL; and T+ Meta‐Temporal FTP with p‐tau181 and GFAP.ConclusionThe significant associations between suprathreshold imaging and plasma biomarkers suggests that, in A+, T+, or (N+) both capture abnormal AD pathophysiology. Further, the absence of an association in the infrathrehold cases supports that the suprathreshold associations are not artifactual. Future work will explore plasma thresholds and concordance between imaging and plasma AT(N).
Optimizing tau‐PET referrals in memory clinics through a blood biomarker workflow
Authors: Brum Wagner Scheeren , Cullen Nicholas , Therriault Joseph , Janelidze Shorena , Rahmouni Nesrine , Stevenson Jenna , Servaes Stijn , Benedet Andrea L. , Zimmer Eduardo R. , Stomrud Erik , Palmqvist Sebastian , Zetterberg Henrik , Frisoni Giovanni , Ashton Nicholas J. , Blennow Kaj , Mattsson‐Carlgren Niklas , Rosa‐Neto Pedro , Hansson Oskar 
Published: January 2025

AbstractBackgroundBlood‐based biomarkers have demonstrated great promise for identifying biomarker‐confirmed Alzheimer's disease. We aimed to evaluate whether blood‐based biomarkers could optimize the referral of memory clinic patients to a tau‐PET exam, which is crucial for prognostic evaluation.MethodThe study measured various plasma biomarkers (Aβ42/Aβ40, pTau181, pTau217, pTau231, NfL, and GFAP) and compared them with tau‐PET scan results in patients with subjective cognitive decline, mild cognitive impairment, or dementia. Participants were sourced from the Swedish BioFINDER‐2 study (548 individuals) and the TRIAD study (179 individuals). Cutoffs for each biomarker were established at 90%, 95%, and 97.5% sensitivity for detecting tau‐PET‐positivity. We then calculated the percentage of patients below these cutoffs (to potentially avoid unnecessary tau‐PET scans) and the tau‐PET‐positivity rate among those above the cutoffs.ResultPlasma pTau217 showed the most promising results. At a 95% sensitivity cutoff in both cohorts, using pTau217 could avoid nearly half of the tau‐PET scans while maintaining a tau‐PET‐positivity rate of approximately 70% in those referred. Furthermore, tau‐PET was strongly associated with subsequent cognitive decline. In the BioFINDER‐2 cohort, tau‐PET predicted cognitive decline only in individuals above the plasma pTau217 referral cutoff, suggesting a more targeted and informative use of tau‐PET scans.ConclusionPlasma pTau217 demonstrates potential as a guiding biomarker for selecting Alzheimer’s disease patients for tau‐PET scans, particularly when accurate prognostic information is clinically valuable. This approach could lead to more efficient and informative use of tau‐PET scans, avoiding unnecessary procedures in patients unlikely to benefit from them.
The New Zealand‐Dementia Prevention Research Clinics: Plasma biomarkers and the Alzheimer’s disease continuum
Authors: Cawston Erin E , Griner Leon , Wong Celestine YE , Myall Daniel J , Ashton Nicholas J. , Gonzalez‐Ortiz Fernando , Blennow Kaj , Zetterberg Henrik , Williams Joanna M , Cutfield Nicholas J , Anderson Tim J , Dalrymple‐Alford John C , Tippett Lynette J ,   The New Zealand‐Dementia Prevention Research Clinics, New Zealand
Published: January 2025

AbstractBackgroundSingle molecule array (Simoa) technology enables the detection of Alzheimer’s disease (AD) neuropathology in blood. This study compared cross‐sectional biomarker profiles for participants from the New Zealand‐Dementia Prevention Research Clinics (NZ‐DPRCs) who spanned the continuum from healthy older adults to a clinical diagnosis of AD.MethodNZ‐DPRC participants were clinically classified as cognitively unimpaired adults (CU, n=34), subjective cognitive decline (SCD, n=65), non‐amnestic mild cognitive impairment (single and multi‐domain, non‐aMCI, n= 23), amnestic MCI (single and multi‐domain, aMCI, n=104), and AD (n=27). A cross‐sectional analysis quantified nine plasma biomarkers using the HD‐X Simoa analyser (Quanterix, Billerica, MA). These were: amyloid‐beta peptides (Aß1‐40; Aß1‐42); total and phosphorylated tau species (total‐tau; brain‐derived tau (BD‐tau); p‐tau181; p‐tau217; p‐tau231); neurofilament light (NfL); and, glial fibrillary acidic protein (GFAP). All biomarkers except for Aß1‐40, Aß1‐42 and the Aß1‐42/Aß1‐40 ratio were natural log‐transformed to approximate normality and improve homogeneity of variance. Plasma biomarkers across the five groups (CU, SCD, aMCI, non‐aMCI and AD) were assessed by linear regression adjusted for age and sex and Tukey’s HSD post‐hoc test was used for pairwise comparisons between classification groupings.ResultThis study included 253 participants from the NZ‐DPRCs (mean [SD] age, 69.8 [8.1] years, 131 females [52%], and 122 males [48%]). Biomarkers p‐tau217, GFAP, p‐tau181, Aß1‐42, BD‐Tau, Aß1‐42/Aß1‐40 ratio, p‐tau231 and NfL, all showed group differences (F(4,246); P<0.05; η2
p = 0.165, 0.109, 0.076, 0.073, 0.059, 0.058, 0.055, 0.044 respectively; Aß1‐40, P=0.06, η2
p = 0.035; total‐tau, P=0.67,η2
p = 0.010). Post‐hoc test for group comparisons showed significant differences (P<0.05) for GFAP between AD vs CU, SCD, non‐aMCI as well as SCD vs MCI; p‐tau181 for AD vs CU, SCD, aMCI, non‐aMCI; p‐tau217 for AD vs CU, SCD, non‐aMCI as well as aMCI vs CU and SCD; p‐tau231 for AD vs non‐aMCI; Aß1‐42 for aMCI vs CU and SCD; Aß1‐42/Aß1‐40 ratio and BD‐tau for SCD vs aMCI. ROC analysis will be reported for the diagnostic accuracy of these blood analytes.ConclusionSimoa‐quantitated blood biomarkers in the NZ‐DPRC cohort showed utility for differentiating cross‐sectional groupings spanning the AD continuum.
Potential Relative Effect of Intensive Systolic Blood Pressure Control on Preventing White Matter Damage Across Age Groups
Authors: Pruzin Jeremy J. , Kern Kyle C , Reboussin David M. , Cushman William C. , Gupta Aditi , Wright Clinton B. , Williamson Jeff D. , Tariot Pierre N. , Nasrallah Ilya M. , Pajewski Nicholas M. 
Published: January 2025

AbstractBackgroundIn the Systolic Blood Pressure Intervention Trial (SPRINT), intensive systolic blood pressure (SBP) lowering slowed progression of white matter injury (WMI) on MRI. We hypothesized that intensive lowering would be equally as effective and may confer greater benefits for brain health at younger ages compared to older ages. We tested whether the relative effects of intensive lowering on WMI differed by age using 2 MRI measures: white matter hyperintensity volume (WMHv) and peak‐width skeletonized mean diffusivity (PSMD) in SPRINT.MethodParticipants were age ≥50 with cardiovascular risk and randomized to intensive (SBP goal <120 mm Hg) or standard treatment (SBP goal <140 mm Hg). We calculated WMHv and PSMD in a subgroup of participants who had a baseline and follow‐up MRI. WMHv were inverse‐hyperbolic sine‐transformed, and the ratio of follow‐up to baseline used to quantify progression. PSMD progression was quantified as a difference. Using mixed‐effects linear models, we estimated the effects of age on relative progression in MRI markers between groups. Age effects were investigated as categorical (<65, 65‐75, or ≥75 years) or continuous.ResultFor participants in the intensive group (n=251 with follow‐up), mean SBP was 122 mmHg versus 135 mmHg in the standard group (n=201) over a median 3.9‐year MRI interval. The largest treatment effect on WMHv progression was found in the <65 age group (i.e. greatest relative reduction with intensive treatment): ‐0.19, 95%CI [‐0.27, ‐0.11]), followed by the 65‐75 age group (‐0.11, [‐0.21, ‐0.01]), and least in the >75 age group (‐0.06, [‐0.20, ‐0.08]) (Figure 1). Intensive treatment resulted in a 73%, 52%, and 20% reduction in WMHv progression respectively, although this was not statistically significant (p=0.21). Analyses of PSMD progression produced similar results (Figure 2). When using age as a continuous measure, the beneficial effect of intensive treatment waned with increasing age (Figure 3) without reaching statistical significance (p=0.19).ConclusionIntensive SBP lowering was equally as effective and may have a greater relative effect on reducing WMI when implemented at younger ages compared to older ages. Large prospective interventional studies that include younger individuals are needed to determine the effects of SBP on white matter integrity across the lifespan.
Plasma p‐tau217 outperforms [18F]FDG‐PET in identifying biological Alzheimer’s disease in atypical and early‐onset dementia
Authors: Socualaya Kely Monica Quispialaya , Therriault Joseph , Aliaga Antonio , Macedo Arthur C. , Rahmouni Nesrine , Arias Jaime Fernandez , Stevenson Jenna , Wang Yi‐Ting , Tissot Cécile , Benedet Andrea L. , Ashton Nicholas J. , Karikari Thomas K , Pascoal Tharick A. , Soucy Jean‐Paul , Gauthier Serge , Vitali Paolo , Zetterberg Henrik , Rosa‐Neto Pedro 
Published: January 2025

AbstractBackgroundBiomarkers promise to significantly improve the differential diagnosis of Alzheimer’s disease (AD). Plasma biomarkers, such as phosphorylated tau (p‐tau), have shown potential in diagnosing AD with high accuracy. Unlike the widely‐used [18 F]FDG‐PET diagnostic biomarker in clinical practice, plasma p‐tau is specific to AD and can provide an affordable and scalable diagnostic tool.MethodWe conducted a retrospective analysis of individuals with atypical dementia, and/or early‐onset dementia cases, who were assessed at a specialized memory clinic. All participants underwent measurements of CSF Aβ42, p‐tau181, and total tau levels, as well as brain [18F]FDG‐PET scans and plasma p‐tau217 measurement. The [18F]FDG‐PET data was visually examined by two nuclear medicine experts to determine whether they were compatible with AD. CSF biomarker results were categorized as either AD biomarker positive or negative. Contingency analysis was performed to assess the relationships between PET scan interpretation and fluid biomarkers (plasma p‐tau217 and CSF p‐tau181 and Aβ42). CSF biomarker and amyloid‐PET were treated as the reference standard.Result81 subjects with atypical dementia had CSF AD biomarker evaluation, [18F]FDG‐PET rating, and plasma p‐tau217 assessment. Both [18F]FDG‐PET and plasma p‐tau217 had high levels of agreement with reference standard AD biomarkers ([18F]FDG‐PET: 71%; plasma p‐tau217: 81%). Although both biomarkers had similar specificity for AD ([18F]FDG‐PET:70%, plasma p‐tau217:70%), plasma p‐tau217 had higher sensitivity for abnormal AD (97%). Overall accuracy was also higher for plasma p‐tau217 (AUC=84%, 95%CI = 0.75‐ 0.93). The same pattern of results was observed when using amyloid‐PET as the reference standard.ConclusionOur study provides evidence that plasma p‐tau217 has excellent diagnostic performance for AD in individuals with early‐onset or atypical dementia evaluated in specialized settings. Nevertheless, the topographical information from [18F]FDG‐PET may give complementary information.
Assessing the efficacy of BETTH‐derived tau thresholds to predict cognitive decline in non‐demented individuals
Authors: Gogola Alexandra , Lopresti Brian J , Snitz Beth E. , Tudorascu Dana , Minhas Davneet S , Dore Vincent , Ikonomovic Milos D , Shaaban C. Elizabeth , Kofler Julia , Matan Cristy , Bourgeat Pierrick , Mason Neale S , Rowe Christopher C. , Aizenstein Howard J , Mathis Chester , Klunk William E , Lopez Oscar L. , Cohen Ann D , Villemagne Victor L 
Published: January 2025

AbstractBackgroundTau PET thresholds should detect early tau deposition and predict cognitive decline. We evaluated the relationships between the BETTH‐derived sensitivity and specificity tau thresholds (Gogola et al, doi:10.2967/jnumed.123.265941, 2023) and cognitive changes in non‐demented participants.MethodFTP scans from 314 non‐demented participants (age 73.17.1 years) and MK scans from 91 non‐demented participants (age 73.65.6 years) were processed and sampled to obtain regional SUVR values. Three cognitive measures were assessed: Clinical Dementia Rating Scale Sum of Boxes (CDR_SoB), Delayed Recall (DR), Logical Memory (LM), and Mini Mental State Examination (MMSE). Cognitive measures collected at the time of scanning were considered timepoint 0 (T0) and the most recently collected measures were considered timepoint 1 (T1). To evaluate the thresholds, SUVRs were split into below (T‐) and above (T+) threshold groups before assessment using both sensitivity (SEN) and specificity thresholds (SPC) for the FTP‐scanned participants and SEN thresholds for MK‐scanned participants, due to the limited number. Regression models between SUVR and change in cognitive measure, adjusted for age, gender, years of education, APOE status, Centiloids, cortical thickness and T0 cognitive score, were evaluated.ResultThe FTP and MK regression model regional and full‐model p‐values are shown in Tables 1 and 2. For FTP, the SEN thresholds yielded greater levels of significance than the SPC thresholds. Additionally, for FTP, the Meta Temporal region was the most significantly associated with cognitive decline in all four metrics (p<0.0001). For MK, Meta Temporal, Inferior Temporal, and Lateral Occipital were significantly associated with CDR_SoB (p<0.001) and Amygdala was significantly associated with LM (p<0.05). No other associations were significant.ConclusionThe superior performance of the Meta Temporal region, for FTP, in all four cognitive measures indicates that cognitive decline is associated with cortical tau deposition. As seen with the MK results, this is particularly true as characterized by CDR_SoB. Moreover, the SEN thresholds were the best overall predictor of cognitive measure change, suggesting that they are the optimal regions and thresholds to apply in clinical diagnosis and therapeutic trials. Further work will assess the Meta Temporal SEN thresholds in relation to cognitive decline within the AT(N) framework.
Use of the hybrid model for developing the concept of dementia friendly in the context of hospitalization
Authors: Munsterman Ellen , Cacchione Pamela Z 
Published: January 2025

AbstractBackgroundPersons living with dementia (PLwD) are hospitalized at greater rates than adults without dementia and experience more adverse outcomes. The use of dementia friendly practices is increasingly common, but definitions for the concept vary. This study describes the fieldwork phase of a hybrid approach to concept development. This phase followed an analysis of the literature that resulted in a working definition for the concept of dementia friendly in the context of hospitalization. This definition incorporates the major recurring attributes found in the literature, including dementia‐related knowledge and education, environmental modifications, person‐centered care, the role of nursing care, and the inclusion of family caregivers. The aim of this study was to refine and confirm the working definition for the concept of dementia friendly in the context of hospitalization, informed by the perceptions of Registered Nurses (RNs) and Certified Nursing Assistants (CNAs).MethodsSemi‐structured interviews were conducted with RNs and CNAs employed on an Acute Care for Elders unit. Interviews were audio‐recorded and professionally transcribed. Directed content analysis was used to analyze the interview data. Transcriptions were coded independently by the two authors and reconciled until agreement was reached.ResultsParticipant demographics are reported in Table 1. Participants were largely in agreement with the definition as written but contributed important nuance to several of the attributes. Regarding staff knowledge and education, participants highlighted the role of hands‐on experience, as well as the delivery and format: synchronous vs. asynchronous. They stressed other characteristics required to coincide with knowledge, including care, compassion, and patience. When discussing the inclusion of family caregivers, participants addressed challenges faced when visiting family members lacked dementia‐specific knowledge.ConclusionThe hybrid approach to concept development is useful for refining complex concepts pertinent to the field of nursing, enabling measurement and further research. In this study, combining qualitative fieldwork with a review of the literature resulted in the refinement of a working definition of the concept of dementia friendly in the context of hospitalization. Dementia friendly practices are necessary to address the unique needs of PLwD in the hospital. A clear definition of the concept will facilitate widespread implementation.
Discovery plasma proteomics analyses identify proteins that interact with plasma ptau231 levels to predict longitudinal episodic memory trajectory over a 9‐year follow‐up period
Authors: Kresge Hailey A. , Tanriverdi Kahraman , Bolton Corey J , Risitano Antonina , Sun Yunyi , Liu Dandan , Pechman Kimberly R. , Gifford Katherine A. , Shah Ravi , Freedman Jane , Hohman Timothy J. , Jefferson Angela L. 
Published: January 2025

AbstractBackgroundThere has been great progress towards identifying plasma biomarkers for Alzheimer’s disease (AD), though few studies have investigated interactions between proteomic measures and AD pathology. We aimed to identify plasma proteins predictive of episodic memory decline, an early clinical sign of AD, and assess whether such associations were altered by the presence of AD pathology.MethodVanderbilt Memory and Aging Project participants (n=350, 73±7 years, 41% female) without dementia at baseline underwent blood draw and serial neuropsychological assessments over 9‐year follow‐up (mean=6.1 years). Proteins were quantified using Olink® Explore 3072. Linear mixed‐effects regressions related protein levels to longitudinal episodic memory composite scores, including interactions with follow‐up time as the terms of interest. Models adjusted for age, sex, race/ethnicity, education, baseline cognitive status, and apolipoprotein E‐ε4 status. Follow‐up analyses included plasma phosphorylated tau 231 (ptau231) as a covariate, tested proteins x ptau231 interactions, and stratified by ptau231 tertile.ResultHigher levels of 15 proteins (GFAP, NEFL, KLK4, SPON1, GH1, CXCL1, OGN, EPHA2, EDA2R, SHBG, CKAP4, DSG2, CD300LG, CLEC5A, NOTCH3) predicted faster decline in episodic memory (pFDR<0.05). With ptau231 included as a covariate, associations between 5 of the proteins (SHBG, CKAP4, DSG2, CD300LG, NOTCH3) and memory attenuated (pFDR>0.05). 38 proteins interacted with ptau231 to predict memory trajectory (pFDR<0.05). Among participants with high ptau23, 24 proteins (LGALS3, FSTL1, SORT1, CHAD, ADAM23, SERPINA3, COL5A1, SYT1, PTPRZ1, CD34, DPP6, POLR2F, PLAUR, SPAG1, PDE4D, PTK7, CSPG4, LRTM2, MEGF10, MYOC, REG4, SPON1, WIF1, EDDM3B) were associated with memory trajectory. Higher protein levels predicted faster decline, except for EDDM3B, for which lower levels predicted faster decline (p<0.05, pFDR>0.05). Among participants with low ptau231, lower levels of 8 proteins (FSTL1, SORT1, IL6R, PTPRM, AHNAK, AMIGO2, IL1RAP, ROBO1) predicted faster decline in memory. Two proteins (FSTL1, SORT1) were associated with memory trajectory in both low and high ptau231 groups, exhibiting opposite directions of effects in the setting of low versus high ptau231 (see Figures).ConclusionResults demonstrate associations between plasma proteomic measures and longitudinal memory trajectory differ by level of AD pathology. To maximize clinical utility of potential biomarkers discovered in plasma proteomic analyses, future studies should assess interactions with AD core biomarkers.
Predicting Future Conversion from Amyloid‐β Negativity to Positivity: Time‐To‐Event Prediction with Machine Learning
Authors: Vahid Amirali , Zamani Jafar , Hosseini Hadi 
Published: January 2025

AbstractBackgroundAlzheimer's disease (AD) is characterized by the gradual accumulation of amyloid‐β (Aβ) plaques and tau tangle pathologies in the brain. Extant studies highlight that Aβ deposition contributes significantly to tau accumulation, leading to neurodegeneration and cognitive decline. Individuals with positive Aβ are more susceptible to AD progression but the precise spatial and temporal pattern of Aβ leading to convergence from negative to positive status is unclear. This study aims to predict the likelihood and the number of years it would take for an individual to transition from baseline negative Aβ to positive (i.e., transition time) using Aβ PET measures with machine learning methods for time‐to‐event and to further identify the most predictive brain regions.MethodPET measures for Aβ SUVR of 446 subjects (216 females, mean age 71 ± 7), initially presenting negative Aβ from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), were utilized. The subjects had a longitudinal follow‐up averaging 4.76 ± 3.5 years, ranging from 1 to 11 years, with 86 transitioning to positive Aβ. For time‐to‐event analysis, three machine learning models— CoxPH, Random Survival Forest (RSF) and Survival XGBoost—were employed to predict the transition time. Their performance was assessed using 5‐fold cross‐validation and evaluated by the concordance index (C‐index). The permutation method was used to determine feature importance as an explainable AI approach.ResultThe C‐index, were 0.71, 0.834, and 0.829 for CoxPH, RSF, and Survival XGBoost, respectively. In the RSF that resulted in the highest accuracy the permutation method highlighted the significant influence of Aβ load in the rostral/medial frontal regions, inferior temporal region and precuneus. Post‐hoc correlation analyses confirmed these results and showed significant negative correlations between Aβ load in these regions and the transition time. Furthermore, based on the Kaplan‐Meier estimate, the median duration for transition time was 3.94 years.ConclusionElevated Aβ levels in the medial and rostral frontal regions strongly predict the time of transition from negative Aβ to preclinical AD (i.e., positive Aβ). These findings highlight the potential of these regions as biomarkers for transition to preclinical AD, emphasizing their prospective application in AD therapy and drug discovery.