Amyloid Positivity Research Articles

Effects of strategic white matter hyperintensities of cholinergic pathways on basal forebrain volume in patients with amyloid-negative neurocognitive disorders

BackgroundThe cholinergic neurotransmitter system is crucial to cognitive function, with the basal forebrain (BF) being particularly susceptible to Alzheimer’s disease (AD) pathology. However, the interaction of white matter hyperintensities (WMH) in cholinergic pathways and BF atrophy without amyloid pathology remains poorly understood.MethodsWe enrolled patients who underwent neuropsychological tests, magnetic resonance imaging, and 18F-florbetaben positron emission tomography due to cognitive impairment at the teaching university hospital from 2015 to 2022. Among these, we selected patients with negative amyloid scans and additionally excluded those with Parkinson’s dementia that may be accompanied by BF atrophy. The WMH burden of cholinergic pathways was quantified by the Cholinergic Pathways Hyperintensities Scale (CHIPS) score, and categorized into tertile groups because the CHIPS score did not meet normal distribution. Segmentation of the BF on volumetric T1-weighted MRI was performed using FreeSurfer, then was normalized for total intracranial volume. Multivariable regression analysis was performed to investigate the association between BF volumes and CHIPS scores.ResultsA total of 187 patients were enrolled. The median CHIPS score was 12 [IQR 5.0; 24.0]. The BF volume of the highest CHIPS tertile group (mean ± SD, 3.51 ± 0.49, CHIPSt3) was significantly decreased than those of the lower CHIPS tertile groups (3.75 ± 0.53, CHIPSt2; 3.83 ± 0.53, CHIPSt1; P = 0.02). In the univariable regression analysis, factors showing significant associations with the BF volume were the CHIPSt3 group, age, female, education, diabetes mellitus, smoking, previous stroke history, periventricular WMH, and cerebral microbleeds. In multivariable regression analysis, the CHIPSt3 group (standardized beta [βstd] = -0.25, P = 0.01), female (βstd = 0.20, P = 0.04), and diabetes mellitus (βstd = -0.22, P < 0.01) showed a significant association with the BF volume. Sensitivity analyses showed a negative correlation between CHIPS score and normalized BF volume, regardless of WMH severity.ConclusionsWe identified a significant correlation between strategic WMH burden in the cholinergic pathway and BF atrophy independently of amyloid positivity and WMH severity. These results suggest a mechanism of cholinergic neuronal loss through the dying-back phenomenon and provide a rationale that strategic WMH assessment may help identify target groups that may benefit from acetylcholinesterase inhibitor treatment.

Predicting Cognitive Decline in Amyloid-Positive Patients With Mild Cognitive Impairment or Mild Dementia.

Cognitive decline rates in Alzheimer disease (AD) vary greatly. Disease-modifying treatments may alter cognitive decline trajectories, rendering their prediction increasingly relevant. We aimed to construct clinically applicable prediction models of cognitive decline in amyloid-positive patients with mild cognitive impairment (MCI) or mild dementia. From the Amsterdam Dementia Cohort, we selected amyloid-positive participants with MCI or mild dementia and at least 2 longitudinal Mini-Mental State Examination (MMSE) measurements. Amyloid positivity was based on CSF AD biomarker concentrations or amyloid PET. We used linear mixed modeling to predict MMSE over time, describing trajectories using a cubic time curve and interactions between linear time and the baseline predictors age, sex, baseline MMSE, APOE ε4 dose, CSF β-amyloid (Aβ) 1-42 and pTau, and MRI total brain and hippocampal volume. Backward selection was used to reduce model complexity. These models can predict MMSE over follow-up or the time to an MMSE value. MCI and mild dementia were modeled separately. Internal 5-fold cross-validation was performed to calculate the explained variance (R2). In total, 961 participants were included (age 65 ± 7 years, 49% female), 310 had MCI (MMSE 26 ± 2) and 651 had mild dementia (MMSE 22 ± 4), with 4 ± 2 measurements over 2 (interquartile range 1-4) years. Cognitive decline rates increased over time for both MCI and mild dementia (model comparisons linear vs squared vs cubic time fit; p < 0.05 favoring a cubic fit). For MCI, backward selection retained age, sex, and CSF Aβ1-42 and pTau concentrations as time-varying effects altering the MMSE trajectory. For mild dementia, retained time-varying effects were Aβ1-42, age, APOE ε4, and baseline MMSE. R2 was 0.15 for the MCI model and 0.26 for mild dementia in internal cross-validation. A hypothetical patient with MCI, baseline MMSE 28, and CSF Aβ1-42 of 925 pg/mL was predicted to reach an MMSE of 20 after 6.0 years (95% CI 5.4-6.7) and after 8.6 years with a hypothetical treatment reducing decline by 30%. We constructed models for MCI and mild dementia that predict MMSE over time. These models could inform patients about their potential cognitive trajectory and the remaining uncertainty and aid in conversations about individualized potential treatment effects.

P-tau217 as a Reliable Blood-Based Marker of Alzheimer's Disease.

Amyloid plaques and tau tangles are the hallmark pathologic features of Alzheimer's disease (AD). Traditionally, these changes are identified in vivo via cerebrospinal fluid (CSF) analysis or positron emission tomography (PET) scans. However, these methods are invasive, expensive, and resource-intensive. To address these limitations, there has been ongoing research over the past decade to identify blood-based markers for AD. Despite the challenges posed by their extremely low concentrations, recent advances in mass spectrometry and immunoassay techniques have made it feasible to detect these blood markers of amyloid and tau deposition. Phosphorylated tau (p-tau) has shown greater promise in reflecting amyloid pathology as evidenced by CSF and PET positivity. Various isoforms of p-tau, distinguished by their differential phosphorylation sites, have been recognized for their ability to identify amyloid-positive individuals. Notable examples include p-tau181, p-tau217, and p-tau235. Among these, p-tau217 has emerged as a superior and reliable marker of amyloid positivity and, thus, AD in terms of accuracy of diagnosis and ability for early prognosis. In this narrative review, we aim to elucidate the utility of p-tau217 as an AD marker, exploring its underlying basis, clinical diagnostic potential, and relevance in clinical care and trials.

Brain amyloid load, subjective memory complaints, and cognitive trajectories in older individuals at risk for dementia.

This study evaluated associations of brain amyloid with 2-year objective and subjective cognitive measures in a trial-ready older general population at risk for dementia. Forty-eight participants in the Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability underwent 11C-Pittsburgh compound B (PiB) positron emission tomography (PET) scans and assessment of cognition (modified Neuropsychological Test Battery [NTB]) and subjective memory complaints (Prospective and Retrospective Memory Questionnaire). Mean age was 71.4 ± 5.06 years, and 20 participants (42%) had positive baseline PiB-PET scans. Amyloid positivity was associated with lower NTB executive function at baseline and less favorable 2-year NTB total score and memory trajectories, but not with other objective or subjective cognitive measures. Overall, there was little cognitive decline during 2 years. Amyloid accumulation may affect objective but not necessarily subjective cognition from a very early at-risk stage, although substantial decline likely requires >2 years to occur.

Temporo-frontoparietal hypoconnectivity as a biomarker for isolated language impairment in mild cognitive impairment: A cross-cohort comparison.

Whether brain functional connectivity (FC) is consistently disrupted in individuals with mild cognitive impairment (MCI) with isolated language impairment (ilMCI), and its potential to differentiate between MCI subtypes remains uncertain. Cross-sectional data from 404 participants in two cohorts (the Chinese Preclinical Alzheimer's Disease Study and the Alzheimer's Disease Neuroimaging Initiative) were analyzed, including neuropsychological tests, resting-state functional magnetic resonance imaging (fMRI), cerebral amyloid positivity, and apolipoprotein E (APOE) status. Temporo-frontoparietal FC, particularly between the bilateral superior temporal pole and the left inferior frontal/supramarginal gyri, was consistently decreased in ilMCI compared to amnestic MCI (aMCI) and normal controls, which was correlated with semantic impairment. Using mean temporo-frontoparietal FC as a classifier could improve accuracy in identifying ilMCI subgroups with positive cerebral amyloid deposition and APOE risk alleles. Temporal-frontoparietal hypoconnectivity was observed in individuals with ilMCI, which may reflect semantic impairment and serve as a valuable biomarker to indicate potential mechanisms of underlying neuropathology. Temporo-frontoparietal hypoconnectivity was observed in impaired language mild cognitive impairment (ilMCI). Temporo-frontoparietal hypoconnectivity may reflect semantic impairment. Temporo-frontoparietal functional connectivity can classify ilMCI subtypes.

Development of thresholds and a visualization tool for use of a blood test in routine clinical dementia practice.

We developed a multimarker blood test result interpretation tool for the clinical dementia practice, including phosphorylated (P-)tau181, amyloid-beta (Abeta)42/40, glial fibrillary acidic protein (GFAP), and neurofilament light (NfL). We measured the plasma biomarkers with Simoa (n=1199), applied LASSO regression for biomarker selection and receiver operating characteristics (ROC) analyses to determine diagnostic accuracy. We validated our findings in two independent cohorts and constructed a visualization approach. P-tau181, GFAP, and NfL were selected. This combination had area under the curve (AUC)=83% to identify amyloid positivity in pre-dementia stages, AUC=87%-89% to differentiate Alzheimer's or controls from frontotemporal dementia, AUC=74%-76% to differentiate Alzheimer's or controls from dementia with Lewy bodies. Highly reproducible AUCs were obtained in independent cohorts. The resulting visualization tool includes UpSet plots to visualize the stand-alone biomarker results and density plots to visualize the biomarker results combined. Our multimarker blood test interpretation tool is ready for testing in real-world clinical dementia settings. We developed a multimarker blood test interpretation tool for clinical dementia practice. Our interpretation tool includes plasma biomarkers P-tau, GFAP, and NfL. Our tool is particularly useful for Alzheimer's and frontotemporal dementia diagnosis.

Optical Coherence Tomography Angiography Retinal Imaging Associations With Burden of Small Vessel Disease and Amyloid Positivity in the Brain.

Alzheimer disease (AD) and other dementias are associated with vascular changes and amyloid deposition, which may be reflected as density changes in the retinal capillaries. These changes may can be directly visualized and quantified with optical coherence tomography angiography (OCTA), making OCTA a potential noninvasive preclinical biomarker of small vessel disease and amyloid positivity. Our objective was to investigate the feasibility of retinal imaging metrics as noninvasive biomarkers of small vessel disease and amyloid positivity in the brain. We investigated associations between OCTA and neuroimaging and cognitive metrics in 41 participants without dementia from the Mayo Clinic Study of Aging and Alzheimer's Disease Research Center. OCTA metrics included superficial, deep, and full retina capillary density of the fovea, parafovea, and macula as well as the area of the foveal avascular zone (FAZ). Neuroimaging metrics included a high burden of white matter hyperintensity (WMH), presence of cerebral microbleeds (CMB), lacunar infarcts, and amyloid positivity as evidenced on positron emission tomography (PET), whereas cognitive metrics included mini-mental status examination (MMSE) score. We performed generalized estimating equations to account for measurements in each eye while controlling for age and sex to estimate associations between OCTA metrics and neuroimaging and cognitive scores. Associations between OCTA and neuroimaging metrics were restricted to the fovea. OCTA showed decreased capillary density with high burden of WMH in both the superficial (P = 0.003), deep (P = 0.004), and full retina (P = 0.01) in the fovea but not the parafovea or whole macula. Similarly, participants with amyloid PET positivity had significantly decreased capillary density in the superficial fovea (P = 0.027) and deep fovea (P = 0.03) but higher density in the superficial parafovea (P = 0.038). Participants with amyloid PET positivity also had a significantly larger FAZ (P = 0.031), whereas in those with high WMH burden the difference did not reach statistical significance (P = 0.075). There was also a positive association between MMSE and capillary density of the full retina within the fovea (P = 0.037) and in the superficial parafovea (P = 0.046). No associations were found between OCTA metrics and presence of CMB or presence of lacunar infarcts. The associations of lower foveal capillary density with cerebral WMH and amyloid positivity suggest that further research is warranted to evaluate for shared mechanisms of disease between small vessel disease and AD pathologies.

Stress testing the Centiloid: Precision and variability of PET quantification of amyloid pathology.

Assessing the potential sources of bias and variability of the Centiloid (CL) scale is fundamental for its appropriate clinical application. We included 533 participants from AMYloid imaging to Prevent Alzheimer's Disease (AMYPAD DPMS) and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts. Thirty-two CL pipelines were created using different combinations of reference region (RR), RR and target types, and quantification spaces. Generalized estimating equations stratified by amyloid positivity were used to assess the impact of the quantification pipeline, radiotracer, age, brain atrophy, and harmonization status on CL. RR selection and RR type impact CL the most, particularly in amyloid-negative individuals. The standard CL pipeline with the whole cerebellum as RR is robust against brain atrophy and differences in image resolution, with 95% confidence intervals below±3.95 CL for amyloid beta positivity cutoffs (CL<24). The standard CL pipeline is recommended for most scenarios. Confidence intervals should be considered when operationalizing CL cutoffs in clinical and research settings. We developed a framework for evaluating Centiloid (CL) variability to different factors. Reference region selection and delineation had the highest impact on CL values. Whole cerebellum (WCB) and whole cerebellum plus brainstem (WCB+BSTM) as reference regions yielded consistent results across tracers. The standard CL pipeline is robust against atrophy and image resolution variation. Estimated within- and between-pipeline variability (95% confidence interval) in absolute CL units.

Real-world performance of plasma p-tau181 in a heterogeneous memory clinic cohort.

In light of clinical trials and disease-modifying therapies, an early identification of patients at-risk of developing Alzheimer's disease (AD) is crucial. Blood-based biomarkers have shown promising results regarding the in vivo detection of the earliest neuropathological changes in AD. Herein, we investigated the ability of plasma p-tau181 to act as a prescreening marker for amyloid positivity in a heterogeneous memory clinic-based cohort. In this retrospective cross-sectional study, we included a total of 115 patients along the clinical AD continuum (mild cognitive impairment [MCI] due to AD, n = 62, probable AD dementia, n = 53). Based on their biomarker status, they were stratified into an amyloid-positive (Aβ+, n = 88) or amyloid-negative cohort (Aβ-, n = 27). Plasma and CSF p-tau181 concentrations were quantified using an ultrasensitive single-molecule array (SIMOA©). Furthermore, age- and sex-adjusted receiver operating characteristic (ROC) curves were calculated and the area under the curve (AUC) of each model was compared using DeLong's test for correlated AUC curves. The median (interquartile range [IQR]) concentration of plasma p-tau181 was significantly higher in Aβ+ patients (3.6 pg/mL [2.5-4.6]), compared with Aβ- patients (1.7 pg/mL [1.2-1.9], p < 0.001). Regarding the distinction between Aβ+ and Aβ- patients and the prediction of amyloid positivity, a high diagnostic accuracy for plasma p-tau181 with an AUC of 0.89 (95% CI = 0.82-0.95) was calculated. Adding the risk factors, age and APOE4, to the model did not significantly improve its performance. Our findings demonstrate that plasma p-tau181 could be a noninvasive and feasible prescreening marker for amyloid positivity in a heterogeneous clinical AD cohort and therefore help in identifying those who would benefit from more invasive assessment of amyloid pathology.

Classifying Alzheimer's Disease Neuropathology Using Clinical and MRI Measurements.

Computer-aided machine learning models are being actively developed with clinically available biomarkers to diagnose Alzheimer's disease (AD) in living persons. Despite considerable work with cross-sectional in vivo data, many models lack validation against postmortem AD neuropathological data. Train machine learning models to classify the presence or absence of autopsy-confirmed severe AD neuropathology using clinically available features. AD neuropathological status are assessed at postmortem for participants from the National Alzheimer's Coordinating Center (NACC). Clinically available features are utilized, including demographics, Apolipoprotein E(APOE) genotype, and cortical thicknesses derived from ante-mortem MRI scans encompassing AD meta regions of interest (meta-ROI). Both logistic regression and random forest models are trained to identify linearly and nonlinearly separable features between participants with the presence (N = 91, age-at-MRI = 73.6±9.24, 38 women) or absence (N = 53, age-at-MRI = 68.93±19.69, 24 women) of severe AD neuropathology. The trained models are further validated in an external data set against in vivo amyloid biomarkers derived from PET imaging (amyloid-positive: N = 71, age-at-MRI = 74.17±6.37, 26 women; amyloid-negative: N = 73, age-at-MRI = 71.59±6.80, 41 women). Our models achieve a cross-validation accuracy of 84.03% in classifying the presence or absence of severe AD neuropathology, and an external-validation accuracy of 70.14% in classifying in vivo amyloid positivity status. Our models show that clinically accessible features, including APOE genotype and cortical thinning encompassing AD meta-ROIs, are able to classify both postmortem confirmed AD neuropathological status and in vivo amyloid status with reasonable accuracies. These results suggest the potential utility of AD meta-ROIs in determining AD neuropathological status in living persons.

Amyloid and SCD jointly predict cognitive decline across Chinese and German cohorts.

Subjective cognitive decline (SCD) in amyloid-positive (Aβ+) individuals was proposed as a clinical indicator of Stage 2 in the Alzheimer's disease (AD) continuum, but this requires further validation across cultures, measures, and recruitment strategies. Eight hundred twenty-one participants from SILCODE and DELCODE cohorts, including normal controls (NC) and individuals with SCD recruited from the community or from memory clinics, underwent neuropsychological assessments over up to 6 years. Amyloid positivity was derived from positron emission tomography or plasma biomarkers. Global cognitive change was analyzed using linear mixed-effects models. In the combined and stratified cohorts, Aβ+ participants with SCD showed steeper cognitive decline or diminished practice effects compared with NC or Aβ- participants with SCD. These findings were confirmed using different operationalizations of SCD and amyloid positivity, and across different SCD recruitment settings. Aβ+ individuals with SCD in German and Chinese populations showed greater global cognitive decline and could be targeted for interventional trials. SCD in amyloid-positive (Aβ+) participants predicts a steeper cognitive decline. This finding does not rely on specific SCD or amyloid operationalization. This finding is not specific to SCD patients recruited from memory clinics. This finding is valid in both German and Chinese populations. Aβ+ older adults with SCD could be a target population for interventional trials.

Longitudinal Phospho-tau217 Predicts Amyloid Positron Emission Tomography in Asymptomatic Alzheimer’s Disease

BackgroundBlood-based AD biomarkers such as plasma P-tau217 are increasingly used in clinical trials as a screening tool.ObjectivesTo assess the utility of an electrochemiluminescence (ECL) immunoassay in predicting brain amyloid PET status in cognitively unimpaired individuals.SettingPlasma samples collected at baseline, week 12, and week 240 or endpoint originated from the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) trial and the companion Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) study.ParticipantsBoth A4 and LEARN enrolled eligible cognitively unimpaired persons 65 to 85 years. Individuals with elevated brain amyloid PET levels were eligible for the A4 Study, while those without elevated brain amyloid PET levels were eligible for the LEARN Study.InterventionParticipants in the A4 Study received intravenous solanezumab (up to 1600 mg) or placebo every 4 weeks. The LEARN Study is an observational study without intervention.MeasurementsPlasma P-tau217 concentration levels from A4 Study participants were measured using an ECL immunoassay. Receiver Operating Characteristic (ROC) curve analysis was performed for each biomarker against amyloid positivity, defined by ≥22 CL and ≥ 33 CL.ResultsReceiver operating characteristic curve (ROC) analysis indicates high diagnostic value of P-tau217 in individuals with amyloid PET ≥ 20 (Area under the ROC (AUROC): 0.87) and ≥ 33 CL (AUROC: 0.89). Repeated testing with the placebo group taken 12 weeks apart (range: 68 to 143 days) and the LEARN participants taken between 1.4 and 1.75 years resulted in a strong positive correlation (Corr. 0.91 (0.90 to 0.92)).ConclusionAn ECL immunoassay testing plasma P-tau217 accurately predicts amyloid PET positivity in cognitively unimpaired individuals. Our future analyses aim to determine if use of this assay may reduce the screening burden of preclinical individuals into anti-amyloid clinical trials.

Synaptic protein CSF levels relate to memory scores in individuals without dementia.

We investigated how cerebrospinal fluid levels of synaptic proteins associate with memory function in normal cognition (CN) and mild cognitive impairment (MCI), and investigated the effect of amyloid positivity on these associations. We included 242 CN (105(43%) abnormal amyloid), and 278 MCI individuals (183(66%) abnormal amyloid) from EMIF-AD MBD and ADNI. For 181 (EMIF-AD MBD) and 36 (ADNI) proteins with a synaptic annotation in SynGO, associations with word learning recall were analysed with linear models. Subsets of synaptic proteins showed lower levels with worse recall in preclinical AD (EMIF-AD MBD: 7, ADNI: 5 proteins, none overlapping), prodromal AD (EMIF-AD MBD only, 27 proteins) and non-AD MCI (EMIF-AD MBD: 1, ADNI: 7 proteins). The majority of these associations were specific to these groups. Synaptic disturbance-related memory impairment occurred very early in AD, indicating it may be relevant to develop therapies targeting the synapse early in the disease.

Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer's disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI.

Abnormal β-amyloid (Aβ) accumulation in the brain is an early indicator of Alzheimer's disease (AD) and is typically assessed through invasive procedures such as PET (positron emission tomography) or CSF (cerebrospinal fluid) assays. As new anti-Alzheimer's treatments can now successfully target amyloid pathology, there is a growing interest in predicting Aβ positivity (Aβ+) from less invasive, more widely available types of brain scans, such as T1-weighted (T1w) MRI. Here we compare multiple approaches to infer Aβ + from standard anatomical MRI: (1) classical machine learning algorithms, including logistic regression, XGBoost, and shallow artificial neural networks, (2) deep learning models based on 2D and 3D convolutional neural networks (CNNs), (3) a hybrid ANN-CNN, combining the strengths of shallow and deep neural networks, (4) transfer learning models based on CNNs, and (5) 3D Vision Transformers. All models were trained on paired MRI/PET data from 1,847 elderly participants (mean age: 75.1 yrs. ± 7.6SD; 863 females/984 males; 661 healthy controls, 889 with mild cognitive impairment (MCI), and 297 with Dementia), scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We evaluated each model's balanced accuracy and F1 scores. While further tests on more diverse data are warranted, deep learning models trained on standard MRI showed promise for estimating Aβ + status, at least in people with MCI. This may offer a potential screening option before resorting to more invasive procedures.

Late Life Depression is Not Associated With Alzheimer-Type Tau: Preliminary Evidence From a Next-Generation Tau Ligand PET-MR Study

To investigate whether tau accumulation is higher in late life depression (LLD) compared to non-depressed cognitively unimpaired (CU) older adults. To situate these findings in the neurodegeneration model of LLD by assessing group differences in tau and grey matter volume (GMV) between LLD, non-depressed CU and mild cognitive impairment due to Alzheimer's Disease (MCI). Monocentric, cross-sectional study. University Psychiatric hospital, memory clinic and outpatient neurology practice. A total of 102 adults over age 60, of whom 19 currently depressed participants with LLD, 19 with MCI and 36 non-depressed CU participants completed neuropsychological testing and tau PET-MR imaging. PET-MRI: 18F-MK-6240 tracer SUVR for tau assessment; 3D T1-weighted structural MRI derived GMV in seven brain regions (temporal, cingulate, prefrontal and parietal regions); amyloid PET to assess amyloid positivity; Neuropsychological test scores: MMSE, RAVLT, GDS, MADRS. ANCOVA and Spearman's rank correlations to investigate group differences in tau and GMV, and correlations with neuropsychological test scores respectively. Compared to non-depressed CU participants, LLD patients showed lower GMV in temporal and anterior cingulate regions but similar tau accumulation and amyloid positivity rate. In contrast, MCI patients had significantly higher tau accumulation in all regions. Tau did not correlate with any neuropsychological test scores in LLD. Our findings suggest AD-type tau is not higher in LLD compared to non-depressed, cognitively unimpaired older adults and appears unlikely to contribute to lower gray matter volume in LLD, further underscoring the need to distinguish major depressive disorder from depressive symptoms occurring in early AD.

Plasma amyloid beta X-42/X-40 ratio and cognitive decline in suspected early and preclinical Alzheimer's disease.

Blood-based biomarkers are a cost-effective and minimally invasive method for diagnosing the early and preclinical stages of amyloid positivity (AP). Our study aims to investigate our novel immunoprecipitation-immunoassay (IP-IA) as a test for predicting cognitive decline. We measured levels of amyloid beta (Aβ)X-40 and AβX-42 in immunoprecipitated eluates from the DELCODE cohort. Receiver-operating characteristic (ROC) curves, regression analyses, and Cox proportional hazard regression models were constructed to predict AP by Aβ42/40 classification in cerebrospinal fluid (CSF) and conversion to mild cognitive impairment (MCI) or dementia. We detected a significant correlation between AßX-42/X-40 in plasma and CSF (r=0.473). Mixed-modeling analysis revealed a substantial prediction of AßX-42/X-40 with an area under the curve (AUC) of 0.81 for AP (sensitivity: 0.79, specificity: 0.74, positive predictive value [PPV]: 0.71, negative predictive value [NPV]: 0.81). In addition, lower AβX-42/X-40 ratios were associated with negative PACC5 slopes, suggesting cognitive decline. Our results suggest that assessing the plasma AβX-42/X-40 ratio via our semiautomated IP-IA is a promising biomarker when examining patients with early or preclinical AD. New plasma Aβ42/Aβ40 measurement using immunoprecipitation-immunoassay Plasma Aβ42/Aβ40 associated with longitudinal cognitive decline Promising biomarker to detect subjective cognitive decline at-risk for brain amyloid positivity.

Symptomatic Clusters Related to Amyloid Positivity in Cognitively Unimpaired Individuals

Background: The NIA-AA Research Framework on Alzheimer’s disease (AD) proposes a transitional stage (stage 2) characterized by subtle cognitive decline, subjective cognitive decline (SCD) and mild neurobehavioral symptoms (NPS). Objective: To identify participant clusters based on stage 2 features and assess their association with amyloid positivity in cognitively unimpaired individuals. Methods: We included baseline data of N = 338 cognitively unimpaired participants from the DELCODE cohort with data on cerebrospinal fluid biomarkers for AD. Classification into the AD continuum (i.e., amyloid positivity, A+) was based on Aβ42/40 status. Neuropsychological test data were used to assess subtle objective cognitive dysfunction (OBJ), the subjective cognitive decline interview (SCD-I) was used to detect SCD, and the Neuropsychiatric Inventory Questionnaire (NPI-Q) was used to assess NPS. A two-step cluster analysis was carried out and differences in AD biomarkers between clusters were analyzed. Results: We identified three distinct participant clusters based on presented symptoms. The highest rate of A+ participants (47.6%) was found in a cluster characterized by both OBJ and SCD. A cluster of participants that presented with SCD and NPS (A+:26.6%) and a cluster of participants with overall few symptoms (A+:19.7%) showed amyloid positivity in a range that was not higher than the expected A+ rate for the age group. Across the full sample, participants with a combination of SCD and OBJ in the memory domain showed a lower Aβ42/ptau181 ratio compared to those with neither SCD nor OBJ. Conclusions: The cluster characterized by participants with OBJ and concomitant SCD was enriched for amyloid pathology.

Protocol for a double-blind placebo-controlled randomised controlled trial assessing the impact of oral semaglutide in amyloid positivity (ISAP) in community dwelling UK adults

IntroductionGlucagon-like peptide-1 receptor agonists (GLP-1 RAs), currently marketed for type 2 diabetes and obesity, may offer novel mechanisms to delay or prevent neurotoxicity associated with Alzheimer’s disease (AD). The impact...

Associations of cerebrovascular disease and Alzheimer’s disease pathology with cognitive decline: Analysis of the National Alzheimer’s Coordinating Center Uniform Data Set

Cerebrovascular disease (CVD) and Alzheimer’s disease (AD) often co-occur and may impact specific cognitive domains. This study’s goal was to determine effects of CVD and AD burden on cross-sectional and longitudinal executive function (EF) and memory in older adults.Longitudinally followed participants from the National Alzheimer Coordinating Center database (n = 3342) were included. Cognitive outcomes were EF and memory composite scores. Baseline CVD presence was defined by moderate-to-severe white matter hyperintensities or lacunar infarct on MRI. Baseline AD pathology was defined by amyloid positivity via PET or CSF. Linear mixed models examined effects of CVD, AD, and time on cognitive outcomes, controlling for sex, education, baseline age, MoCA score, and total number of study visits.At baseline, CVD associated with lower EF (p < 0.001), while AD associated with lower EF and memory (ps < 0.001). Longitudinally only AD associated with faster declines in memory and EF (ps < 0.001).These results extend our understanding of CVD and AD pathology, highlighting that CVD does not necessarily indicate accelerated decline.

AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.

Background Visual assessment of amyloid PET scans relies on the availability of radiologist expertise, whereas quantification of amyloid burden typically involves MRI for processing and analysis, which can be computationally expensive. Purpose To develop a deep learning model to classify minimally processed brain PET scans as amyloid positive or negative, evaluate its performance on independent data sets and different tracers, and compare it with human visual reads. Materials and Methods This retrospective study used 8476 PET scans (6722 patients) obtained from late 2004 to early 2023 that were analyzed across five different data sets. A deep learning model, AmyloidPETNet, was trained on 1538 scans from 766 patients, validated on 205 scans from 95 patients, and internally tested on 184 scans from 95 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) fluorine 18 (18F) florbetapir (FBP) data set. It was tested on ADNI scans using different tracers and scans from independent data sets. Scan amyloid positivity was based on mean cortical standardized uptake value ratio cutoffs. To compare with model performance, each scan from both the Centiloid Project and a subset of the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) study were visually interpreted with a confidence level (low, intermediate, high) of amyloid positivity/negativity. The area under the receiver operating characteristic curve (AUC) and other performance metrics were calculated, and Cohen κ was used to measure physician-model agreement. Results The model achieved an AUC of 0.97 (95% CI: 0.95, 0.99) on test ADNI 18F-FBP scans, which generalized well to 18F-FBP scans from the Open Access Series of Imaging Studies (AUC, 0.95; 95% CI: 0.93, 0.97) and the A4 study (AUC, 0.98; 95% CI: 0.98, 0.98). Model performance was high when applied to data sets with different tracers (AUC ≥ 0.97). Other performance metrics provided converging evidence. Physician-model agreement ranged from fair (Cohen κ = 0.39; 95% CI: 0.16, 0.60) on a sample of mostly equivocal cases from the A4 study to almost perfect (Cohen κ = 0.93; 95% CI: 0.86, 1.0) on the Centiloid Project. Conclusion The developed model was capable of automatically and accurately classifying brain PET scans as amyloid positive or negative without relying on experienced readers or requiring structural MRI. Clinical trial registration no. NCT00106899 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Bryan and Forghani in this issue.