Amyloid Status Research Articles

Perivascular space enlargement accelerates in ageing and Alzheimer’s disease pathology: evidence from a three-year longitudinal multicentre study

BackgroundPerivascular space (PVS) enlargement in ageing and Alzheimer’s disease (AD) and the drivers of such a structural change in humans require longitudinal investigation. Elucidating the effects of demographic factors, hypertension, cerebrovascular dysfunction, and AD pathology on PVS dynamics could inform the role of PVS in brain health function as well as the complex pathophysiology of AD.MethodsWe studied PVS in centrum semiovale (CSO) and basal ganglia (BG) computationally over three to four annual visits in 503 participants (255 females; meanage = 70.78 ± 5.78) of the ongoing observational multicentre “DZNE Longitudinal Cognitive Impairment and Dementia Study” (DELCODE) cohort. We analysed data from subjects who were cognitively unimpaired (n = 401), had amnestic mild cognitive impairment (n = 71), or had AD (n = 31). We used linear mixed-effects modelling to test for changes of PVS volumes in relation to cross-sectional and longitudinal age, as well as sex, years of education, hypertension, white matter hyperintensities, AD diagnosis, and cerebrospinal-fluid-derived amyloid (A) and tau (T) status (available for 46.71%; A-T-/A + T-/A + T + n = 143/48/39).ResultsPVS volumes increased significantly over follow-ups (CSO: B = 0.03 [0.02, 0.05], p < 0.001; BG: B = 0.05 [0.03, 0.07], p < 0.001). PVS enlargement rates varied substantially across subjects and depended on the participant’s age, white matter hyperintensities volumes, and amyloid and tau status. PVS volumes were higher across elderly participants, regardless of region of interest (CSO: B = 0.12 [0.02, 0.21], p = 0.017; BG: B = 0.19 [0.09, 0.28], p < 0.001). Faster BG-PVS enlargement related to lower baseline white matter hyperintensities volumes (ρspearman = -0.17, pFDR = 0.001) and was more pronounced in individuals who presented with combined amyloid and tau positivity versus negativity (A + T + > A-T-, pFDR = 0.004) or who were amyloid positive but tau negative (A + T + > A + T-, pFDR = 0.07). CSO-PVS volumes increased at a faster rate with amyloid positivity as compared to amyloid negativity (A + T-/A + T + > A-T-, pFDR = 0.021).ConclusionOur longitudinal evidence supports the relevance of PVS enlargement in presumably healthy ageing as well as in AD pathology. We further discuss the region-specific involvement of white matter hyperintensities and neurotoxic waste accumulation in PVS enlargement and the possibility of additional factors contributing to PVS progression. A comprehensive understanding of PVS dynamics could facilitate the understanding of pathological cascades and might inform targeted treatment strategies.Trial registrationGerman Clinical Trials Register DRKS00007966. Registered 04.05.2015 – retrospectively registered, https://drks.de/search/en/trial/DRKS00007966.

Tracer-Separator: A Deep Learning Model for Brain PET Dual-Tracer (18F-FDG and Amyloid) Separation.

Multiplexed PET imaging revolutionized clinical decision-making by simultaneously capturing various radiotracer data in a single scan, enhancing diagnostic accuracy and patient comfort. Through a transformer-based deep learning, this study underscores the potential of advanced imaging techniques to streamline diagnosis and improve patient outcomes. The research cohort consisted of 120 patients spanning from cognitively unimpaired individuals to those with mild cognitive impairment, dementia, and other mental disorders. Patients underwent various imaging assessments, including 3D T1-weighted MRI, amyloid PET scans using either 18F-florbetapir (FBP) or 18F-flutemetamol (FMM), and 18F-FDG PET. Summed images of FMM/FBP and FDG were used as proxy for simultaneous scanning of 2 different tracers. A SwinUNETR model, a convolution-free transformer architecture, was trained for image translation. The model was trained using mean square error loss function and 5-fold cross-validation. Visual evaluation involved assessing image similarity and amyloid status, comparing synthesized images with actual ones. Statistical analysis was conducted to determine the significance of differences. Visual inspection of synthesized images revealed remarkable similarity to reference images across various clinical statuses. The mean centiloid bias for dementia, mild cognitive impairment, and healthy control subjects and for FBP tracers is 15.70 ± 29.78, 0.35 ± 33.68, and 6.52 ± 25.19, respectively, whereas for FMM, it is -6.85 ± 25.02, 4.23 ± 23.78, and 5.71 ± 21.72, respectively. Clinical evaluation by 2 readers further confirmed the model's efficiency, with 97 FBP/FMM and 63 FDG synthesized images (from 120 subjects) found similar to ground truth diagnoses (rank 3), whereas 3 FBP/FMM and 15 FDG synthesized images were considered nonsimilar (rank 1). Promising sensitivity, specificity, and accuracy were achieved in amyloid status assessment based on synthesized images, with an average sensitivity of 95 ± 2.5, specificity of 72.5 ± 12.5, and accuracy of 87.5 ± 2.5. Error distribution analyses provided valuable insights into error levels across brain regions, with most falling between -0.1 and +0.2 SUV ratio. Correlation analyses demonstrated strong associations between actual and synthesized images, particularly for FMM images (FBP: Y = 0.72X + 20.95, R2 = 0.54; FMM: Y = 0.65X + 22.77, R2 = 0.77). This study demonstrated the potential of a novel convolution-free transformer architecture, SwinUNETR, for synthesizing realistic FDG and FBP/FMM images from summation scans mimicking simultaneous dual-tracer imaging.

Unveiling the hippocampal subfield changes across the Alzheimer's disease continuum: a systematic review of neuroimaging studies.

Studies exploring the hippocampal subfield atrophy in Alzheimer's disease (AD) have shown contradictory results. This review aims to disentangle such heterogeneity by investigating the dynamic changes of hippocampal subfields across the AD continuum. We systematically searched the PubMed and EMBASE databases for case-control studies. Selected studies included investigations of biomarker-based amyloid status and reported data on hippocampal subfield atrophy using advanced MRI techniques. Twelve studies were included. Despite high heterogeneity, a distinguishable pattern of vulnerability of hippocampal subfields can be recognized from the cognitively unimpaired phase to the dementia stage, shedding light on hippocampal changes with disease progression. Consistent findings revealed atrophy in the subiculum and presubiculum, along with a potential increase in volume in the cornu ammonis (CA) among the cognitively unimpaired group, a feature not observed in patients experiencing subjective cognitive decline. Atrophy in the subiculum, presubiculum, CA 1-4, and the dentate gyrus characterized the mild cognitive impairment stage, with a more pronounced severity in the progression to dementia.

Assessing clinical progression measures in Alzheimer's disease trials: A systematic review and meta-analysis.

Assessing treatments for Alzheimer's disease (AD) relies on reliable tools for measuring AD progression. In this analysis, we evaluate the sensitivity of clinical progression measures in AD within randomized controlled trials (RCTs) with confirmed positive amyloid (Aβ+) status prior to trial enrollment. Excluding trials targeting non-cognitive symptoms, we conducted meta-analyses on progression measures from 25 selected RCTs using R version 4.2.0, along with the metafor and emmeans libraries. The Functional Activities Questionnaire (FAQ) demonstrated the greatest sensitivity over 12weeks. Other cognitive measures demonstrated lower sensitivity. The integrated Alzheimer's Disease Rating Scale (iADRS) and Clinical Dementia Rating-Sum of Boxes (CDR-SB) seemed more effective than their individual cognitive components. Neuropsychiatric measures were the least sensitive in measuring progression. Functional measures generally outperformed other measure categories. Purely cognitive domain-based measures were suboptimal for tracking early AD progression. Ideally, future measures should incorporate both cognitive and functional components to enhance sensitivity. Concerns remain regarding the limitations of current outcome measures used in AD clinical trials, particularly their sensitivity in the early and preclinical stages of the disease, which hampers their reliability as indicators of AD progression. The Functional Activities Questionnaire (FAQ) demonstrated the most substantial weighted mean change over 12weeks, followed by the Mini-Mental State Examination (MMSE). Functional measures outperformed other measure categories. Composite scores of integrated Alzheimer's Disease Rating Scale and Clinical Dementia Rating-Sum of Boxes are more sensitive to change than their individual cognitive components, possibly driven by the functional components of the score. Neuropsychiatric measures analyzed in this study appeared to be the least sensitive in measuring progression.

Head-to-head comparison of leading blood tests for Alzheimer's disease pathology.

Blood tests have the potential to improve the accuracy of Alzheimer's disease (AD) clinical diagnosis, which will enable greater access to AD-specific treatments. This study compared leading commercial blood tests for amyloid pathology and other AD-related outcomes. Plasma samples from the Alzheimer's Disease Neuroimaging Initiative were assayed with AD blood tests from C2N Diagnostics, Fujirebio Diagnostics, ALZPath, Janssen, Roche Diagnostics, and Quanterix. Outcomes measures were amyloid positron emission tomography (PET), tau PET, cortical thickness, and dementia severity. Logistic regression models assessed the classification accuracies of individual or combined plasma biomarkers for binarized outcomes, and Spearman correlations evaluated continuous relationships between individual plasma biomarkers and continuous outcomes. Measures of plasma p-tau217, either individually or in combination with other plasma biomarkers, had the strongest relationships with all AD outcomes. This study identified the plasma biomarker analytes and assays that most accurately classified amyloid pathology and other AD-related outcomes. Plasma p-tau217 measures most accurately classified amyloid and tau status. Plasma Aβ42/Aβ40 had relatively low accuracy in classification of amyloid status. Plasma p-tau217 measures had higher correlations with cortical thickness than NfL. Correlations of plasma biomarkers with dementia symptoms were relatively low.

Development and assessment of algorithms for predicting brain amyloid positivity in a population without dementia

BackgroundThe accumulation of amyloid-β (Aβ) peptide in the brain is a hallmark of Alzheimer’s disease (AD), occurring years before symptom onset. Current methods for quantifying in vivo amyloid load involve invasive or costly procedures, limiting accessibility. Early detection of amyloid positivity in non-demented individuals is crucial for aiding early AD diagnosis and for initiating anti-amyloid immunotherapies at early stages. This study aimed to develop and validate predictive models to identify brain amyloid positivity in non-demented patients, using routinely collected clinical data.MethodsPredictive models for amyloid positivity were developed using data from 853 non-demented participants in the MEMENTO cohort. Amyloid levels were measured potentially repeatedly during study course through Positron Emision Tomography or CerebroSpinal Fluid analysis.The probability of amyloid positivity was modelled using mixed-effects logistic regression. Predictors included demographic information, cognitive assessments, visual brain MRI evaluations of hippocampal atrophy and lobar microbleeds, AD-related blood biomarkers (Aβ42/40 and P-tau181), and ApoE4 status. Models were subjected to internal cross-validation and external validation using data from the Amsterdam Dementia Cohort. Performance also was evaluated in a subsample that met the main criteria of the Appropriate Use Recommendations (AUR) for lecanemab.ResultsThe most effective model incorporated demographic data, cognitive assessments, ApoE status, and AD-related blood biomarkers, achieving AUCs of 0.82 [95%CI 0.81-0.82] in MEMENTO sample and 0.90 [95%CI 0.86-0.94] in the external validation sample. This model significantly outperformed a reference model based solely on demographic and cognitive data, with an AUC difference in MEMENTO of 0.10 [95%CI 0.10-0.11]. A similar model without ApoE genotype achieved comparable discriminatory performance. MRI markers did not improve model performance. Performances in AUR of lecanemab subsample were comparable.ConclusionA predictive model integrating demographic, cognitive, and blood biomarker data offers a promising method to help identify amyloid status in non-demented patients. ApoE genotype and brain MRI data were not necessary for strong discriminatory ability, suggesting that ApoE genotyping may be deferred when assessing the risk-benefit ratio of immunotherapies in amyloid-positive patients who desire treatment. The integration of this model into clinical practice could reduce the need for lumbar puncture or PET examinations to confirm amyloid status.

18F]PI-2620 Tau PET signal across the aging and Alzheimer’s disease clinical spectrum

Abstract [18F]PI-2620 is a second generation tracer that has shown high binding affinity for tau aggregation in Alzheimer’s disease (AD). However, [18F]PI-2620 signal in a large sample spanning the healthy aging and AD clinical spectrum as well as the stability of signal across different acquisition time windows have not yet been examined. Here, amyloid negative (Aβ-) cognitively unimpaired (CU; n=49), amyloid positive (Aβ+) CU (n=37), CU individuals with unknown amyloid status (n=5), mild cognitive impairment (MCI; n=14), dementia due to AD (n=19), and non-AD neurodegenerative disorder (n=54) participants were scanned with [18F]PI-2620 using a 45-75 min and/or 60-90 min acquisition time window. The impact of acquisition time on standardized uptake value ratio (SUVR) magnitude was first quantified with linear mixed models and in participants and regions with high [18F]PI-2620 signal, SUVRs increased linearly up to 0.04 SUVR with each additional 5 min past injection time. We then accounted for differences in acquisition time using a voxelwise correction approach and showed high correlations (all rs&amp;gt;0.986) between SUVRs calculated from 45-75 min data and SUVRs from 60-90 min data that were interpolated to the 45-75 min scale in 15 participants who were scanned across both time windows. Using real and interpolated 45-75 min data, we next examined [18F]PI-2620 signal in Braak regions of interest and an off-target binding region (putamen) in Aβ- and Aβ+ CU, Aβ+ MCI, and Aβ+ AD dementia (n=115) and showed that SUVRs in all Braak regions increased with greater disease severity. Within CU, higher Braak I SUVR was significantly associated with greater CSF pTau-181 (n=35), and higher SUVRs were significantly associated with worse memory and language (n=57). Thus, voxelwise acquisition time corrections can be applied to combine [18F]PI-2620 datasets collected at different post-injection times, and once acquisition time is accounted for, [18F]PI-2620 signal shows the expected increases across the AD spectrum and can be used for detection of early tau elevations.

Executive function and cortical thickness in biomarker aMCI

Introduction Memory deficits are the primary symptom in amnestic Mild Cognitive Impairment (aMCI); however, executive function (EF) deficits are common. The current study examined EF in aMCI based upon amyloid status (A+/A−) and regional atrophy in signature areas of Alzheimer’s disease (AD). Method Participants included 110 individuals with aMCI (A+ = 66; A− = 44) and 33 cognitively healthy participants (HP). EF was assessed using four neuropsychological assessment measures. The cortical thickness of the AD signature areas was calculated using structural MRI data. Results A + had greater EF deficits and cortical atrophy relative to A − in the supramarginal gyrus and superior parietal lobule. A − had greater EF deficits relative to HP, but no difference in signature area cortical thickness. Discussion The current study found that the degree of EF deficits in aMCI are a function of amyloid status and cortical thinning in the parietal cortex.

A-086 Clinical Performance of a Plasma ATN Panel for Assessment of Alzheimer’s Disease

Abstract Background In recent years, the progression of Alzheimer’s disease has widely been characterized through the utilization of an ATN classification system consisting of biomarkers for three areas of neuropathologic change: development of Amyloid associated plaques, formation of neurofibrillary tangles associated with Tau proteins, and onset of Neurodegeneration. However, the assessment of biomarkers related to each of these stages has predominantly relied on neuroimaging or CSF measurements which are costly and invasive. With the advent of high sensitivity immunoassays, measurements of ATN biomarkers in venous plasma has become possible and would provide a more accessible and affordable tool to assist physicians with the diagnosis of AD. Methods Assessment of amyloid status was performed by determining the ratio of amyloid-beta 1-42 to amyloid-beta 1-40 (Aβ42/40) in plasma where measurements for individual analytes were performed in parallel on a SysmexTM HISCL-5000 platform. Assessments of tau and neurodegeneration were performed using a Roche cobas® e801 platform to measure phosphorylated-tau181 (pTau181) and neurofilament light chain (NfL), respectively. As these assays have not yet received FDA approval, each was validated internally using CLSI guidance. The clinical efficacy of each assay was investigated using 200 clinical specimens with amyloid status defined using positron emission tomography (PET) imaging. Assay results were analyzed using receiver operator characteristic (ROC) analysis with respect to amyloid PET results. In addition, results from the clinical cohort were also assessed with respect: amyloid PET centiloid results, clinically defined cognitive status, and mental state as determined using a Mini-Mental State Exam (MMSE). Results ROC analysis of clinical sample results from validated assays produced an area-under-the-curve (AUC) of 0.941 for Aβ42/40, 0.847 for pTau181, and 0.666 for NfL. These results are consistent with amyloid PET status as well as the progression of AD along the ATN continuum. With respect to Aβ+ subjects, Aβ42/40 results did not change due to worsening cognitive status, increasing amyloid PET results, or worsening MMSE scores. Levels of pTau181, however, were found to increase with worsening cognitive status (p &amp;lt; 0.01), increasing amyloid PET centiloid results (p &amp;lt; 0.05), as well as worsening MMSE scores (p &amp;lt; 0.05). In addition, NfL levels become elevated with worsening MMSE scores (p &amp;lt; 0.01). Conclusions The clinical utility of the plasma ATN panel was found to be appropriate for assisting with diagnosis of AD and is now available for physician use.

Predicting regional tau accumulation with machine learning‐based tau‐PET and advanced radiomics

AbstractINTRODUCTIONAlzheimer's disease is partially characterized by the progressive accumulation of aggregated tau‐containing neurofibrillary tangles. Although the association between accumulated tau, neurodegeneration, and cognitive decline is critical for disease understanding and clinical trial design, we still lack robust tools to predict individualized trajectories of tau accumulation. Our objective was to assess whether brain imaging biomarkers of flortaucipir‐positron emission tomography (PET), in combination with clinical and genomic measures, could predict future pathological tau accumulation.METHODSWe quantified the disease profile of participants (N = 276) using a comprehensive set of descriptors, including clinical/demographic (age, diagnosis, amyloid status, sex, race, ethnicity), genetic (apolipoprotein E [APOE]‐ε4), and flortaucipir‐PET imaging measures (regional flortaucipir standardized uptake value ratio [SUVr] and comprehensive radiomic texture features extracted from Automated Anatomical Labeling template regions). We trained an AdaBoost machine learning algorithm in a 2:1 split train‐test configuration to derive a prognostic index that (i) stratifies individualized brain regions including global (AD‐signature region) and lobar regions (frontal, occipital, parietal, temporal) into stable/slow‐ and fast‐progressors based on future tau accumulation, and (ii) forecasts individualized regional annualized‐rate‐of‐change in flortaucipir‐PET SUVr. Further, we developed an adaptive model incorporating flortaucipir‐PET measurements from the baseline and intermediate timepoints to predict annualized‐rate‐of‐change.RESULTSIn binary classification for predicting stable/slow‐ versus fast‐progressors, the area‐under‐the‐receiver‐operating‐characteristic curve was 0.86 in the AD‐signature region and 0.83, 0.82, 0.84, and 0.83 in frontal, occipital, parietal, and temporal regions, respectively. The trained models successfully predicted annualized‐rate‐of‐change of flortaucipir‐PET regional flortaucipir SUVr in AD‐signature and lobar regions (Pearson‐correlation [R]: AD‐signature = 0.73; frontal = 0.73; occipital = 0.71; parietal = 0.70; temporal = 0.69). The models’ performance in predicting annualized‐rate‐of‐change slightly increased when imaging features from intermediate timepoints were used in the adaptive setting (R: AD‐signature = 0.79; frontal = 0.87; occipital = 0.83; parietal = 0.74; temporal = 0.82).DISCUSSIONTaken together, our results propose a robust approach to predict future tau accumulation that may improve the ability to enroll, stratify, and gauge efficacy in clinical trial participants.Highlights Machine learning predicts the future rate of tau accumulation in Alzheimer's disease. Tau prediction in lobar/global regions benefits from diverse multimodal features. This prognostic index can serve as a sensitive tool for patient stratification.

Can integration of Alzheimer's plasma biomarkers with MRI, cardiovascular, genetics, and lifestyle measures improve cognition prediction?

There is increasing interest in Alzheimer's disease related plasma biomarkers due to their accessibility and scalability. We hypothesized that integrating plasma biomarkers with other commonly used and available participant data (MRI, cardiovascular factors, lifestyle, genetics) using machine learning (ML) models can improve individual prediction of cognitive outcomes. Further, our goal was to evaluate the heterogeneity of these predictors across different age strata. This longitudinal study included 1185 participants from the Mayo Clinic Study of Aging who had complete plasma analyte work-up at baseline. We used the Quanterix Simoa immunoassay to measure neurofilament light, Aβ1-42 and Aβ1-40 (used as Aβ42/Aβ40 ratio), glial fibrillary acidic protein, and phosphorylated tau 181 (p-tau181). Participants' brain health was evaluated through gray and white matter structural MRIs. The study also considered cardiovascular factors (hyperlipidemia, hypertension, stroke, diabetes, chronic kidney disease), lifestyle factors (area deprivation index, body mass index, cognitive and physical activities), and genetic factors (APOE, single nucleotide polymorphisms, and polygenic risk scores). An ML model was developed to predict cognitive outcomes at baseline and decline (slope). Three models were created: a base model with groups of risk factors as predictors, an enhanced model included socio-demographics, and a final enhanced model by incorporating plasma and socio-demographics into the base models. Models were explained for three age strata: younger than 65 years, 65-80 years, and older than 80 years, and further divided based on amyloid positivity status. Regardless of amyloid status the plasma biomarkers showed comparable performance (R² = 0.15) to MRI (R² = 0.18) and cardiovascular measures (R² = 0.10) when predicting cognitive decline. Inclusion of cardiovascular or MRI measures with plasma in the presence of socio-demographic improved cognitive decline prediction (R² = 0.26 and 0.27). For amyloid positive individuals Aβ42/Aβ40, glial fibrillary acidic protein and p-tau181 were the top predictors of cognitive decline while Aβ42/Aβ40 was prominent for amyloid negative participants across all age groups. Socio-demographics explained a large portion of the variance in the amyloid negative individuals while the plasma biomarkers predominantly explained the variance in amyloid positive individuals (21% to 37% from the younger to the older age group). Plasma biomarkers performed similarly to MRI and cardiovascular measures when predicting cognitive outcomes and combining them with either measure resulted in better performance. Top predictors were heterogeneous between cross-sectional and longitudinal cognition models, across age groups, and amyloid status. Multimodal approaches will enhance the usefulness of plasma biomarkers through careful considerations of a study population's socio-demographics, brain and cardiovascular health.

3T sodium-MRI as predictor of neurocognition in nondemented older adults: a cross sectional study.

Dementia is a burgeoning global problem. Novel magnetic resonance imaging (MRI) metrics beyond volumetry may bring new insight and aid clinical trial evaluation of interventions early in the Alzheimer's disease course to complement existing imaging and clinical metrics. To determine whether: (i) normalized regional sodium-MRI values (Na-SI) are better predictors of neurocognitive status than volumetry (ii) cerebral amyloid PET status improves modelling. Nondemented older adult (>60 years) volunteers of known Alzheimer's Disease Assessment Scale (ADAS-Cog11), Mini-Mental State Examination (MMSE) and Consortium to Establish a Registry for Alzheimer's Disease (CERAD) neurocognitive test scores, ApolipoproteinE (APOE) e4 +/- cerebral amyloid PET status were prospectively recruited for 3T sodium-MRI brain scans. Left and right hippocampal, entorhinal and precuneus volumes and Na-SI (using the proportional intensity scaling normalization method with field inhomogeneity and partial volume corrections) were obtained after segmentation and co-registration of 3D-T1-weighted proton images. Descriptive statistics, correlation and best-subset regression analyses were performed. In our 76 nondemented participants (mean(standard deviation) age 75(5) years; woman 47(62%); cognitively unimpaired 54/76(71%), mildly cognitively impaired 22/76(29%)), left hippocampal Na-SI, not volume, was preferentially in the best models for predicting MMSE (Odds Ratio (OR) = 0.19(Confidence Interval (CI) = 0.07,0.53), P-value = 0.001) and ADAS-Cog11 (Beta(B) = 1.2(CI = 0.28,2.1), P-value = 0.01) scores. In the entorhinal analysis, right entorhinal Na-SI, not volume, was preferentially selected in the best model for predicting ADAS-Cog11 (B = 0.94(CI = 0.11,1.8), P-value = 0.03). While right entorhinal Na-SI and volume were both selected for MMSE modelling (Na-SI OR = 0.23(CI = 0.09,0.6), P-value = 0.003; volume OR = 2.6(CI = 1.0,6.6), P-value = 0.04), independently, Na-SI explained more of the variance (Na-SI R 2 = 10.3; volume R 2 = 7.5). No imaging variable was selected in the best CERAD models. Adding cerebral amyloid status improved model fit (Akaike Information Criterion increased 2.0 for all models, P-value < 0.001-0.045). Regional Na-SI were more predictive of MMSE and ADAS-Cog11 scores in our nondemented older adult cohort than volume, hippocampal more robust than entorhinal region of interest. Positive amyloid status slightly further improved model fit.

Investigating patient eligibility for anti-amyloid monoclonal antibody treatment of Alzheimer's disease: real-world data from an Austrian psychiatric memory clinic population.

Pharmacological treatment options for patients with dementia owing to Alzheimer's disease are limited to symptomatic therapy. Recently, the US Food and Drug Administration approved the monoclonal antibody lecanemab for the treatment of amyloid-positive patients with mild cognitive impairment (MCI) and early Alzheimer´s dementia. European approval is expected in 2024. Data on the applicability and eligibility for treatment with anti-amyloid monoclonal antibodies outside of a study population are lacking. This study examined eligibility criteria for lecanemab in a real-world memory clinic population between 1 January 2022 and 31 July 2023. We conducted a retrospective, single-centre study applying the clinical trial eligibility criteria for lecanemab to out-patients of a specialised psychiatric memory clinic. Eligibility for anti-amyloid treatment was assessed following the phase 3 inclusion and exclusion criteria and the published recommendations for lecanemab. The study population consisted of 587 out-patients. Two-thirds were diagnosed with Alzheimer's disease (probable or possible Alzheimer's disease dementia in 43.6% of cases, n = 256) or MCI (23%, n = 135), and 33.4% (n = 196) were diagnosed with dementia or neurocognitive disorder owing to another aetiology. Applying all lecanemab eligibility criteria, 11 (4.3%) patients with dementia and two (1.5%) patients with MCI would have been eligible for treatment with this compound, whereas 13 dementia (5.1%) and 14 (10.4%) MCI patients met clinical inclusion criteria, but had no available amyloid status. Even in a memory clinic with a good infrastructure and sufficient facilities for dementia diagnostics, most patients do not meet the eligibility criteria for treatment with lecanemab.

Hyperspectral Retinal Imaging as a Non-Invasive Marker to Determine Brain Amyloid Status.

As an extension of the central nervous system (CNS), the retina shares many similarities with the brain and can manifest signs of various neurological diseases, including Alzheimer's disease (AD). To investigate the retinal spectral features and develop a classification model to differentiate individuals with different brain amyloid levels. Sixty-six participants with varying brain amyloid-β protein levels were non-invasively imaged using a hyperspectral retinal camera in the wavelength range of 450-900 nm in 5 nm steps. Multiple retina features from the central and superior views were selected and analyzed to identify their variability among individuals with different brain amyloid loads. The retinal reflectance spectra in the 450-585 nm wavelengths exhibited a significant difference in individuals with increasing brain amyloid. The retinal features in the superior view showed higher inter-subject variability. A classification model was trained to differentiate individuals with varying amyloid levels using the spectra of extracted retinal features. The performance of the spectral classification model was dependent upon retinal features and showed 0.758-0.879 accuracy, 0.718-0.909 sensitivity, 0.764-0.912 specificity, and 0.745-0.891 area under curve for the right eye. This study highlights the spectral variation of retinal features associated with brain amyloid loads. It also demonstrates the feasibility of the retinal hyperspectral imaging technique as a potential method to identify individuals in the preclinical phase of AD as an inexpensive alternative to brain imaging.

PrecivityAD2™ Blood Test: Analytical Validation of an LC-MS/MS Assay for Quantifying Plasma Phospho-tau217 and Non-Phospho-tau217 Peptide Concentrations That Are Used with Plasma Amyloid-β42/40 in a Multianalyte Assay with Algorithmic Analysis for Detecting Brain Amyloid Pathology.

Alzheimer's disease (AD) is a progressive irreversible neurodegenerative disorder that represents a major global public health concern. Traditionally, AD is diagnosed using cerebrospinal fluid biomarker analysis or brain imaging modalities. Recently, less burdensome, more widely available blood biomarker (BBM) assays for amyloid-beta (Aβ42/40) and phosphorylated-tau concentrations have been found to accurately identify the presence/absence of brain amyloid plaques and tau tangles and have helped to streamline AD diagnosis. However, few BBMs have been rigorously analytically validated. Herein, we report the analytical validation of a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) multiplex method for quantifying plasma phosphorylated-tau217 (p-tau217) and non-phosphorylated-tau217 (np-tau217) peptide concentrations. We combined the p-tau217/np-tau217 concentrations ratio (%p-tau217) and the previously validated LC-MS/MS multiplex assay for plasma Aβ42/40 into a new multianalyte assay with algorithmic analysis (MAAA; PrecivityAD2™ test) that identifies brain amyloid status based on brain amyloid positron emission tomography. We found (a) the %p-tau217 assay is precise, accurate, sensitive, and linear over a wide analytical measurement range, and free from carryover and interference; (b) the pre-analytical specimen collection, processing, storage, and shipping conditions that maintain plasma tau peptide stability; and (c) using the measured analytical imprecision for plasma Aβ42/40 and p-tau217/np-tau217 levels in a worst-case scenario model, the PrecivityAD2 test algorithm for amyloid pathology classification changed for only 3.5% of participants from brain amyloid positive to negative, or from negative to positive. The plasma sample preparation and LC-MS/MS methods underlying the PrecivityAD2 test are suitable for use in the clinical laboratory and valid for the test's intended purpose: to aid in the diagnostic evaluation of individuals aged 55 and older with signs or symptoms of mild cognitive impairment or dementia.

Analysis of individual alpha frequency in a large cohort from a tertiary memory center.

Precise and timely diagnosis is crucial for the optimal use of emerging disease-modifying treatments for Alzheimer disease (AD). Electroencephalography (EEG), which is noninvasive and cost-effective, can capture neural abnormalities linked to various dementias. This study explores the use of individual alpha frequency (IAF) derived from EEG as a diagnostic and prognostic tool in cognitively impaired patients. This retrospective study included 375 patients from the tertiary Memory Clinic of IRCCS San Raffaele Hospital, Milan, Italy. Participants underwent clinical and neuropsychological assessments, brain imaging, cerebrospinal fluid biomarker analysis, and resting-state EEG. Patients were categorized by amyloid status, the AT(N) classification system, clinical diagnosis, and mild cognitive impairment (MCI) progression to AD dementia. IAF was calculated and compared among study groups. Receiver operating characteristic (ROC) analysis was used to calculate its discriminative performance. IAF was higher in amyloid-negative subjects and varied significantly across AT(N) groups. ROC analysis confirmed IAF's ability to distinguish A-T-N- from the A+T+N+ and A+T-N+ groups. IAF was lower in AD and Lewy body dementia patients compared to MCI and other dementia types, with moderate discriminatory capability. Among A+ MCI patients, IAF was significantly lower in those who converted to AD within 2 years compared to stable MCI patients and predicted time to conversion (p < 0.001, R = 0.38). IAF is a valuable tool for dementia diagnosis and prognosis, correlating with amyloid status and neurodegeneration. It effectively predicts MCI progression to AD, supporting its use in early, targeted interventions in the context of disease-modifying treatments.

Clinical utility of diffusion MRI-derived measures of cortical microstructure in a real-world memory clinic setting.

To investigate cortical microstructural measures from diffusion MRI as "neurodegeneration" markers that could improve prognostic accuracy in mild cognitive impairment (MCI). The prognostic power of Amyloid/Tau/Neurodegeneration (ATN) biomarkers to predict progression from MCI to AD or non-AD dementia was investigated. Ninety patients underwent clinical evaluation (follow-up interval 32 ± 18 months), lumbar puncture, and MRI. Participants were grouped by clinical stage and cerebrospinal fluid Amyloid and Tau status. T1-structural and diffusion MRI scans were analyzed to calculate diffusion metrics related to cortical columnar structure (AngleR, ParlPD, PerpPD+), cortical mean diffusivity, and fractional anisotropy. Statistical tests were corrected for multiple comparisons. Prognostic power was assessed using receiver operating characteristic (ROC) analysis and related indices. A progressive increase of whole-brain cortical diffusion values was observed along the AD continuum, with all A+ groups showing significantly higher AngleR than A-T-. Investigating clinical progression to dementia, the AT biomarkers together showed good positive predictive value (with 90.91% of MCI A+T+ converting to dementia) but poor negative predictive value (with 40% of MCI A-T- progressing to a mix of AD and non-AD dementias). Adding whole-brain AngleR as an N marker, produced good differentiation between stable and converting MCI A-T- patients (0.8 area under ROC curve) and substantially improved negative predictive value (+21.25%). Results support the clinical utility of cortical microstructure to aid prognosis, especially in A-T- patients. Further work will investigate other complexities of the real-world clinical setting, including A-T+ groups. Diffusion MRI measures of neurodegeneration may complement fluid AT markers to support clinical decision-making.

Amyloid PET disclosure in subjective cognitive decline: Patient experiences over time.

We disclosed amyloid positron emission tomography (PET) results in individuals with subjective cognitive decline (SCD) and studied patient experiences and outcomes over a 6-month period. Fifty-seven participants from the Subjective Cognitive Impairment Cohort (SCIENCe) (66±8 years, 21 [37%] F, Mini-Mental State Examination 29±1, 15 [26%] amyloid positive [A+]) completed questionnaires 1 week prior (T0), 1 day after (T1), and 6 months after amyloid PET disclosure (T2). Questionnaires addressed patient-reported experiences and outcomes. Independent of amyloid status, participants were satisfied with the consultation (scale 1-10; 7.9±1.7) and information provided (scale 1-4; T1: 3.3±0.9, T2: 3.2±0.8). After 6 months, A+ participants reported more information needs (45%vs. 12%, p=0.02). Independent of amyloid status, decision regret (scale 1-5; A+: 1.5±0.9, A-: 1.4±0.6, p=0.53) and negative emotions (negative affect, uncertainty, anxiety) were low (all p>0.15 and Pinteraction>0.60). Participants with SCD valued amyloid PET disclosure positively, regardless of amyloid status. The need for information after 6 months, which was stronger in A+ individuals, underscores the importance of follow-up. Participants with subjective cognitive decline (SCD) positively valued amyloid positron emission tomography (PET) disclosure. Participants with SCD experienced low levels of decision regret. We did not observe an increase in negative emotions. After 6 months, amyloid-positive individuals wanted more information.

Plasma pTau217 predicts continuous brain amyloid levels in preclinical and early Alzheimer's disease.

This study investigated the potential of phosphorylated plasma Tau217 ratio (pTau217R) and plasma amyloid beta (Aβ) 42/Aβ40 in predicting brain amyloid levels measured by positron emission tomography (PET) Centiloid (CL) for Alzheimer's disease (AD) staging and screening. Quantification of plasma pTau217R and Aβ42/Aβ40 employed immunoprecipitation-mass spectrometry. CL prediction models were developed on a cohort of 904 cognitively unimpaired, preclinicaland early AD subjects and validated on two independent cohorts. Models integrating pTau217R outperformed Aβ42/Aβ40 alone, predicting amyloid levels up to 89.1 CL. High area under the receiver operating characteristic curve (AUROC) values (89.3% to 94.7%) were observed across a broad CL range (15 to 90). Utilizing pTau217R-based models for low amyloid levels reduced PET scans by 70.5% to 78.6%. pTau217R effectively predicts brain amyloid levels, surpassing cerebrospinal fluid Aβ42/Aβ40's range. Combining it with plasma Aβ42/Aβ40 enhances sensitivity for low amyloid detection, reducing unnecessary PET scans and expanding clinical utility. NCT02956486 (MissionAD1), NCT03036280 (MissionAD2), NCT04468659 (AHEAD3-45), NCT03887455 (ClarityAD) HIGHLIGHTS: Phosphorylated plasma Tau217 ratio (pTau217R) effectively predicts amyloid-PET Centiloid (CL) across a broad spectrum. Integrating pTau217R with Aβ42/Aβ40 extends the CL prediction upper limit to 89.1 CL. Combined model predicts amyloid status with high accuracy, especially in cognitively unimpaired individuals. This modelidentifies subjects above or below various CL thresholds with high accuracy. pTau217R-based models significantly reduce PET scans by up to 78.6% for screening out individuals with no/low amyloid.

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.