Amyloid Positivity Research Articles

Brain structural indicators of β-amyloid neuropathology

Recent efforts demonstrated the efficacy of identifying early-stage neuropathology of Alzheimer’s disease (AD) through lumbar puncture cerebrospinal fluid assessment and positron emission tomography (PET) radiotracer imaging. These methods are effective yet are invasive, expensive, and not widely accessible. We extend and improve the multiscale structural mapping (MSSM) procedure to develop structural indicators of β-amyloid neuropathology in preclinical AD, by capturing both macrostructural and microstructural properties throughout the cerebral cortex using a structural MRI. We find that the MSSM signal is regionally altered in clear positive and negative cases of preclinical amyloid pathology (N = 220) when cortical thickness alone or hippocampal volume is not. It exhibits widespread effects of amyloid positivity across the posterior temporal, parietal, and medial prefrontal cortex, surprisingly consistent with the typical pattern of amyloid deposition. The MSSM signal is significantly correlated with amyloid PET in almost half of the cortex, much of which overlaps with regions where beta-amyloid accumulates, suggesting it could provide a regional brain ‘map’ that is not available from systemic markers such as plasma markers.

Rescreening on RBANS Delayed Memory Index? Forget About It!

To assess the value of rescreening patients with Alzheimer's disease who do not meet the inclusion criteria for the Repeatable Battery for the Assessment of Neuropsychological Status Delayed Memory Index (RBANS DMI) at the initial assessment. Participants (aged 50-85years, without dementia, Mini-Mental State Examination score ≥22, valid Clinical Dementia Rating [CDR] global score, and amyloid status at baseline) were identified in the European Prevention of Alzheimer's Dementia database. Changes from baseline in RBANS DMI were estimated using a mixed model for repeated measurements. Logistic regressions were used to estimate the probability of participants with baseline RBANS DMI 86-95 having RBANS DMI ≤85, CDR global score ≥0.5, and amyloid positivity at 6 and 12 months. There was significant variability in the change in RBANS DMI scores over time (median change at 6months: 2.0). An estimated 15% of participants with RBANS DMI 86-95 at baseline progressed to ≤85 at 6 months; 8% also achieved CDR global score ≥0.5 and 5% were also amyloid positive. The results from our analysis indicate that there is limited value in rescreening patients based on their initial RBANS DMI score.

Relationship Between Amyloid Positivity and Sleep Characteristics in the Elderly With Subjective Cognitive Decline.

Alzheimer's disease (AD) is a neurodegenerative disease characterized by a progressive decline in cognition and performance of daily activities. Recent studies have attempted to establish the relationship between AD and sleep. It is believed that patients with AD pathology show altered sleep characteristics years before clinical symptoms appear. This study evaluated the differences in sleep characteristics between cognitively asymptomatic patients with and without some amyloid burden. Sleep characteristics of 76 subjects aged 60 years or older who were diagnosed with subjective cognitive decline (SCD) but not mild cognitive impairment (MCI) or AD were measured using Fitbit® Alta HR, a wristwatch-shaped wearable device. Amyloid deposition was evaluated using brain amyloid plaque load (BAPL) and global standardized uptake value ratio (SUVR) from fluorine-18 florbetaben positron emission tomography. Each component of measured sleep characteristics was analyzed for statistically significant differences between the amyloid-positive group and the amyloid-negative group. Of the 76 subjects included in this study, 49 (64.5%) were female. The average age of the subjects was 70.72±6.09 years when the study started. 15 subjects were classified as amyloid-positive based on BAPL. The average global SUVR was 1.598±0.263 in the amyloid-positive group and 1.187±0.100 in the amyloid-negative group. Time spent in slow-wave sleep (SWS) was significantly lower in the amyloid-positive group (39.4±13.1 minutes) than in the amyloid-negative group (49.5±13.1 minutes) (p=0.009). This study showed that SWS is different between the elderly SCD population with and without amyloid positivity. How SWS affects AD pathology requires further research.

Sleep and physical activity measures are associated with resting-state network segregation in non-demented older adults

Greater physical activity and better sleep are associated with reduced risk of cognitive decline and dementia among older adults, but little is known about their combined associations with measures of brain function and neuropathology. This study investigated potential independent and interactive cross-sectional relationships between actigraphy-estimated total volume of physical activity (TVPA) and sleep patterns [i.e., total sleep time (TST), sleep efficiency (SE)] with resting-state functional magnetic resonance imaging (rs-fMRI) measures of large scale network connectivity and positron emission tomography (PET) measures of amyloid-β. Participants were 135 non-demented older adults from the BIOCARD study (116 cognitively normal and 19 with mild cognitive impairment; mean age = 70.0 years). Using multiple linear regression analyses, we assessed the association between TVPA, TST, and SE with connectivity within the default-mode, salience, and fronto-parietal control networks, and with network modularity, a measure of network segregation. Higher TVPA and SE were independently associated with greater network modularity, although the positive relationship of SE with modularity was only present in amyloid-negative individuals. Additionally, higher TVPA was associated with greater connectivity within the default-mode network, while greater SE was related to greater connectivity within the salience network. In contrast, longer TST was associated with lower network modularity, particularly among amyloid-positive individuals, suggesting a relationship between longer sleep duration and greater network disorganization. Physical activity and sleep measures were not associated with amyloid positivity. These data suggest that greater physical activity levels and more efficient sleep may promote more segregated and potentially resilient functional networks and increase functional connectivity within specific large-scale networks and that the relationship between sleep and functional networks connectivity may depend on amyloid status.

Korea-Registries to Overcome Dementia and Accelerate Dementia Research (K-ROAD): A Cohort for Dementia Research and Ethnic-Specific Insights.

Dementia, particularly Alzheimer's disease, is a significant global health concern, with early diagnosis and treatment development being critical goals. While numerous cohorts have advanced dementia research, there is a lack of comprehensive data on ethnic differences, particularly for the Korean population. The Korea-Registries to Overcome Dementia and Accelerate Dementia Research (K-ROAD) aims to establish a large-scale, hospital-based dementia cohort to address this gap, with a focus on understanding disease progression, developing early diagnostics, and supporting treatment advancements specific to the Korean population. K-ROAD comprises multiple prospective cohorts. Participants underwent clinical evaluations, neuroimaging, and biomarker analysis, with data collected on a range of clinical and genomic markers. As of December 2023, K-ROAD has recruited over 5,800 participants, including individuals across the Alzheimer's clinical syndrome, subcortical vascular cognitive impairment, and frontotemporal dementia spectra. Preliminary findings highlight significant ethnic differences in amyloid positivity, cognitive decline, and biomarker profiles, compared to Western cohorts. The K-ROAD cohort offers a unique and critical resource for dementia research, providing insights into ethnic-specific disease characteristics and biomarker profiles. These findings will contribute to the development of personalized diagnostic and therapeutic approaches to dementia, enhancing global understanding of the disease.

Predicting Brain Amyloid Status Using the National Institute of Health Toolbox (NIHTB) for Assessment of Neurological and Behavioral Function.

Amyloid-beta (Aβ) plaque is a neuropathological hallmark of Alzheimer's disease (AD). As anti-amyloid monoclonal antibodies enter the market, predicting brain amyloid status is critical to determine treatment eligibility. To predict brain amyloid status utilizing machine learning approaches in the Advancing Reliable Measurement in Alzheimer's Disease and Cognitive Aging (ARMADA) study. ARMADA is a multisite study that implemented the National Institute of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in older adults with different cognitive ability levels (normal, mild cognitive impairment, early-stage dementia of the AD type). Participants across various sites were involved in the ARMADA study for validating the NIHTB. 199 ARMADA participants had either PET or CSF information (mean age 76.3 ± 7.7, 51.3% women, 42.3% some or complete college education, 50.3% graduate education, 88.9% White, 33.2% with positive AD biomarkers). We used cognition, emotion, motor, sensation scores from NIHTB, and demographics to predict amyloid status measured by PET or CSF. We applied LASSO and random forest models and used the area under the receiver operating curve (AUROC) to evaluate the ability to identify amyloid positivity. The random forest model reached AUROC of 0.74 with higher specificity than sensitivity (AUROC 95% CI:0.73 - 0.76, Sensitivity 0.50, Specificity 0.88) on the held-out test set; higher than the LASSO model (0.68 (95% CI:0.68 - 0.69)). The 10 features with the highest importance from the random forest model are: picture sequence memory, cognition total composite, cognition fluid composite, list sorting working memory, words-in-noise test (hearing), pattern comparison processing speed, odor identification, 2-minutes-walk endurance, 4-meter walk gait speed, and picture vocabulary. Overall, our model revealed the validity of measurements in cognition, motor, and sensation domains, in associating with AD biomarkers. Our results support the utilization of the NIH toolbox as an efficient and standardizable AD biomarker measurement that is better at identifying amyloid negative (i.e., high specificity) than positive cases (i.e., low sensitivity).

The Use of Event Related Potentials to Predict Amyloid PET Status Among Patients from a Memory Disorders Clinic.

Amyloid positron emission tomography (PET) scans provide in vivo evidence of Alzheimer's disease (AD); however, their high cost limits their use in standard clinical care. Event related potentials (ERPs) may represent an inexpensive and non-invasive additional method for detecting AD pathology. We investigated whether ERPs, along with neuropsychological data, serve as predictors of amyloid PET status in patients with memory complaints. Veterans aged 50-100 were recruited from a memory disorders clinic. Participants underwent a neuropsychological battery and an ERP auditory oddball protocol. Twenty-eight patients had a positive amyloid PET scan, and thirty-nine patients had a negative scan. ERP-P200 target amplitude and P200 standard latency were predictors of amyloid PET status. When submitting to ROC analysis, P200 standard latency exhibited the highest specificity and sensitivity in predicting amyloid PET positivity, correctly classifying the amyloid PET status for 86% of patients. ERP-P200 measures are strong indicators of amyloid-β presence in patients from a memory disorder clinic. Increased P200 amplitude and decreased P200 latency in patients with a positive amyloid PET scan may be attributed to hyperactivation of perceptual bottom-up processes compensating for AD-related synaptic loss in the fronto-parietal networks.

Lecanemab: Appropriate Use Recommendations by Korean Dementia Association.

Lecanemab (product name Leqembi®) is an anti-amyloid monoclonal antibody treatment approved for use in Korea for patients with mild cognitive impairment (MCI) or mild dementia due to Alzheimer's disease. The Korean Dementia Association has created recommendations for the appropriate use of lecanemab to assist clinicians. These recommendations include selecting patients for administration, necessary pre-administration tests and preparations, administration methods, monitoring for amyloid related imaging abnormalities (ARIA), and communication with patients and caregivers. Lecanemab is recommended for patients with MCI or mild dementia who confirmed positive amyloid biomarkers, and should not be administered to patients with severe hypersensitivity to lecanemab or those unable to undergo magnetic resonance imaging (MRI) evaluation. To predict the risk of ARIA before administration, apolipoprotein E genotyping is conducted, and regular brain MRI evaluations are recommended to monitor for ARIA during treatment. The most common adverse reactions are infusion-related reactions, which require appropriate management upon occurrence. Additional caution is needed when co-administering with anticoagulants or tissue plasminogen activator due to the risk of macrohemorrhage. Clinicians should consider the efficacy and necessary conditions for administration, as well as the safety of lecanemab, to make a comprehensive decision regarding its use.

SARCOPENIA AND MOBILITY DYSFUNCTION AS INDICATORS OF INCREASED ADRD RISK IN ASYMPTOMATIC DISEASE

Abstract Sarcopenia, the age-related loss of muscle mass and function, affects up to 30% of older adults. In addition to being an established component of frailty and predictor of mobility dysfunction, sarcopenia is associated with cognitive decline and increased risk of cognitive impairment, with ~70% individuals with late-stage dementia having sarcopenia. In this study, we investigated whether sarcopenia differentiates high- vs low- ADRD risk among 141 community-dwelling, older adults who were healthy controls (HC) or had mild cognitive impairment (MCI) (65% female; mean Age=70±11yr; mean Educ=16±3yr). Using the Brain Health Platform (BHP) developed at the Comprehensive Center for Brain Health, we categorized participants based on measures of resilience, vulnerability, cognitive performance as: Low-risk HC (36.9%); High-risk HC (12.8%), and MCI (50.4%). Amyloid positivity increased across risk groups (20% in Low-risk HC; 33.3% in High-risk HC; 39.1% in MCI). Prevalence of sarcopenia was 26.7% and increased by ADRD risk (12.2% vs 18.8% vs 39.1%, p=0.004). The MCI group had lower flexibility compared to both the low-risk (p=0.024) and high-risk HC (p=0.039) groups. Timed Up and Go (TUG) score increased with risk group (p< 0.001MCI vs Low-risk HC; p=0.005MCI vs High-risk HC). Both mobility measures (i.e., flexibility, TUG) showed good discrimination for MCI vs Low-risk HC [AUC=0.77flexibility; AUC=0.78TUG] as well as for MCI vs High-risk HC [AUC=0.71flexibility; AUC=0.73TUG). Findings suggest that sarcopenia and other markers of mobility dysfunction have the potential to identify asymptomatic individuals at increased risk of ADRD, who could benefit from early intervention.

Innate Immune Activation in Mouse Models of Amyloidosis and Tauopathy

For many years scientists have felt that innate immune activation in the brain (some call this inflammation) played a role in Alzheimer's dementia, and may provide a useful drug target. This hypothesis has been reinforced by the genetic studies which identified many innate immune gene variants that modulate the risk of developing Alzheimer's. Our initial studies testing the hypothesis examined immune activation in models of amyloidosis using lipopolysaccharide and vaccination. Surprisingly, both led to reduced amyloidosis. Additional studies regulated immune activation using gene therapy in both models of amyloidosis and tauopathy. In many instances, an intervention that benefited amyloidosis was found to exacerbate tauopathy and vice versa. In studies of innate immune markers in human CSF, there are some biomarkers that are lower in individuals who are amyloid positive compared to those without amyloid, but which increase in cases with both amyloid and tau positivity. Although the results regarding central nervous system innate immune activation are sometimes discordant with respect to amyloid or tau pathology, recent data find that systemic immune activation by serial exposures to PAMPs, results in both increased tau pathology and increased amyloid pathology. In conclusion, innate immune activation can have differential effects on amyloidosis or tauopathy and the effects of central and systemic inflammation may also be different. Specific targeting of select aspects of innate immunity will be necessary at different stages of the disease to moderate the progression of Alzheimer's disease.

Free Water MR Imaging of White Matter Microstructural Changes is a Sensitive Marker of Amyloid Positivity in Alzheimer's Disease.

Extracellular free water (FW) resulting from white matter degeneration limits the sensitivity of diffusion tensor imaging (DTI) in predicting Alzheimer's disease (AD). To evaluate the sensitivity of FW-DTI in detecting white matter microstructural changes in AD. To validate the effectiveness of FW-DTI indices to predict amyloid-beta (Aβ) positivity in mild cognitive impairment (MCI) subtypes. Retrospective. Thirty-eight Aβ-negative cognitively healthy (CH) controls (68.74 ± 8.28 years old, 55% female), 15 Aβ-negative MCI patients (MCI-n) (68.87 ± 8.83 years old, 60% female), 29 Aβ-positive MCI patients (MCI-p) (73.03 ± 7.05 years old, 52% female), and 29 Aβ-positive AD patients (72.93 ± 9.11 years old, 55% female). 3.0T; DTI, T1-weighted, T2-weighted, T2 star-weighted angiography, and Aβ PET (18F-florbetaben or 11C-PIB). FW-corrected and standard diffusion indices were analyzed using trace-based spatial statistics. Area under the curve (AUC) in distinguishing MCI subtypes were compared using support vector machine (SVM). Chi-squared test, one-way analysis of covariance, general linear regression analyses, nonparametric permutation tests, partial Pearson's correlation, receiver operating characteristic curve analysis, and linear SVM. A P value <0.05 was considered statistically significant. Compared with CH/MCI-n/MCI-p, AD showed significant change in tissue compartment indices of FW-DTI. No difference was found in the FW index among pair-wise group comparisons (the minimum FWE-corrected P = 0.114). There was a significant association between FW-DTI indices and memory and visuospatial function. The SVM classifier with tissue radial diffusivity as an input feature had the best classification performance of MCI subtypes (AUC = 0.91), and the classifying accuracy of FW-DTI was all over 89.89%. FW-DTI indices prove to be potential biomarkers of AD. The classification of MCI subtypes based on SVM and FW-DTI indices has good accuracy and could help early diagnosis. 4 TECHNICAL EFFICACY: Stage 2.

Predicting positron emission tomography brain amyloid positivity using interpretable machine learning models with wearable sensor data and lifestyle factors

BackgroundDeveloping a screening method for identifying individuals at higher risk of elevated brain amyloid burden is important to reduce costs and burden to patients in clinical trials on Alzheimer’s disease or the clinical setting. We developed machine learning models using objectively measured lifestyle factors to predict elevated brain amyloid burden on positron emission tomography.MethodsOur prospective cohort study of non-demented, community-dwelling older adults aged ≥ 65 years was conducted from August 2015 to September 2019 in Usuki, Oita Prefecture, Japan. One hundred and twenty-two individuals with mild cognitive impairment or subjective memory complaints (54 men and 68 women, median age: 75.50 years) wore wearable sensors and completed self-reported questionnaires, cognitive test, and positron emission tomography imaging at baseline. Moreover, 99 individuals in the second year and 61 individuals in the third year were followed up. In total, 282 eligible records with valid wearable sensors, cognitive test results, and amyloid imaging and data on demographic characteristics, living environments, and health behaviors were used in the machine learning models. Amyloid positivity was defined as a standardized uptake value ratio of ≥ 1.4. Models were constructed using kernel support vector machine, Elastic Net, and logistic regression for predicting amyloid positivity. The mean score among 10 times fivefold cross-validation repeats was utilized for evaluation.ResultsIn Elastic Net, the mean area under the receiver operating characteristic curve of the model using objectively measured lifestyle factors alone was 0.70, whereas that of the models using wearable sensors in combination with demographic characteristics and health and life environment questionnaires was 0.79. Moreover, 22 variables were common to all machine learning models.ConclusionOur machine learning models are useful for predicting elevated brain amyloid burden using readily-available and noninvasive variables without the need to visit a hospital.Trial registrationThis prospective study was conducted in accordance with the Declaration of Helsinki and was approved by the local ethics committee of Oita University Hospital (UMIN000017442). A written informed consent was obtained from all participants. This research was performed based on the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline.

Sensitivity of NIH Toolbox‐Cognition Battery task performance to amyloid positivity status in cognitively normal older adults

AbstractBackgroundAmong cognitively normal older adults, high brain amyloid burden may signal the presence of early Alzheimer’s disease processes. While research has shown subtle neuropsychological performance differences among cognitively intact older adults with and without elevated brain amyloid, early investigation has not found similar between‐group differences on the NIH Toolbox‐Cognition Battery (NIHTB‐CB; Snitz et al., 2020). Here, we utilize data from a longitudinal observational driving study to evaluate potential differences in NIHTB‐CB task performance among older adults determined to be cognitively normal via clinical research consensus, based on brain amyloid status.MethodData from cognitively normal adults age 65+ who completed an amyloid Positron Emission Tomography scan, NIHTB‐CB tasks, and additional neuropsychological testing through the Michigan Alzheimer’s Disease Research Center were analyzed. Amyloid positivity was determined based on a centiloid scale (≤10 indicating amyloid negativity; ≥20 amyloid positivity). Thirty participants were amyloid negative and 17 were amyloid positive. Nine individuals with intermediate amyloid values were excluded. Group differences in NIHTB‐CB performance were analyzed via independent sample t‐tests. Linear regressions were used to assess demographic and NIHTB‐CB task/neuropsychological performance predictors of amyloid positivity.ResultThere were no demographic differences between the groups. Overall, amyloid negative participants outperformed their amyloid positive peers on NIHTB‐CB List Sorting and backward Digit Span tasks (p’s&lt;0.05), both of which are measures of working memory. There were no other group differences in cognitive performance, including episodic memory. The final NIHTB‐CB regression model only included NIHTB List Sorting, which accounted for 16.2% of the variance in amyloid status (F = 8.69, p = 0.005). Digit Span backward similarly accounted for 16.6% of the variance in amyloid status (F = 6.39, p = 0.017).ConclusionAmong cognitively normal older adults with known amyloid status, working memory performance, but not episodic memory performance, differentiated amyloid positive and negative groups. This is consistent with research demonstrating an association between elevated amyloid and executive dysfunction, but not memory impairment, in cognitively normal adults (Tideman et al., 2022). NIHTB‐CB’s List Sorting Working Memory is a complex, challenging sequencing task that appears sensitive to amyloid positivity in older adults and task performance may serve as a marker for early prediction of Alzheimer’s disease.

Plasma ALZpath p‐tau217 for the identification of amyloid and tau positivity

AbstractBackgroundIn the last 5 years, immunoassays for the quantification of phosphorylated tau (p‐tau) in blood have proven to accurately identify Alzheimer’s disease (AD) pathology with important implications for primary care management and therapeutic trials. Comparisons of p‐tau epitopes (p‐tau181, p‐tau217, p‐tau231) show small differences in diagnostic accuracy, however, often p‐tau217 is preferred, citing its larger fold‐change in symptomatic patients and lower rate of false positives in amyloid‐negative individuals. This study describes the performance of a novel Single molecule array (Simoa) for p‐tau217 (p‐tau217ALZpath)MethodWe included 355 participants from Translational Biomarkers in Aging and Dementia (TRIAD) participants, which was representative of the AD continuum. Further, 425 cognitively unimpaired individuals, with longitudinal plasma measures (≤8 years), were included from Wisconsin Registry for Alzheimer’s Prevention (WRAP). All plasma samples were measured for ALZpath p‐tau217ALZpath, a validated Simoa assay at the University of Gothenburg, Sweden. In TRIAD, amyloid positivity (A+) was defined as [18F]AZD‐4694 SUVR &gt;1.5 or CSF Aβ42/40 &lt;0.068. In WRAP, A+ was defined by [11C]PiB &gt;1.16. 18F‐MK‐6240 determined tau status (T+) both cohorts (TRIAD, &gt;1.24 SUVR; WRAP, &gt;1.3).ResultIn TRIAD, p‐tau217ALZpath determined A+ (AUC = 0.957, 0.935‐978) and T+ (AUC = 0.952, 0.929–0.976) individuals with high accuracy. A sensitivity analysis, including only participants with imaging (amyloid and tau) and CSF biomarkers (p‐tau181, p‐tau205, p‐tau217, Aβ42/40), p‐tau217ALZpath demonstrated equivalent accuracies in determining A+ (Figure 1A) and T+ (Figure 1B) than these high‐performing modalities. In addition, plasma p‐tau217AlzPATH demonstrated a good accuracy to identify T+ from all A+ participants (AUC = 0.839, 0.767‐910). Concentration cut‐points derived in WRAP to identify A+ (&gt;0.676 pg/mL; PPV = 87.2%) and A– (&lt;0.235 pg/mL; NPV = 98.2%) were directly tested in TRIAD with high accuracy (&gt;0.676 pg/mL, PPV = 98.8%; &lt;0.235 pg/mL, NPV = 95.1%. In WRAP, longitudinal analysis (Figure 2) demonstrated that over 8‐years, p‐tau217AlzPATH increased only in A+ individuals and more steeply in A+T+ participants (βestimate = 0.1 pg/mL per year; P&lt;0.0001) compared to A+T‐ (βestimate = 0.022 pg/mL per year, P = 0.0008).ConclusionThe novel p‐tau217ALZpath immunoassay demonstrates high accuracy to determine amyloid and tau pathology across all stages of AD continuum. Clinical cut‐points to identify A+ or A– with high PPV and NPV are directly transferable across cohorts.

Combining plasma p‐tau231 and glial fibrillary acidic protein produces higher discriminative accuracy for amyloid positivity than other blood‐based biomarker combinations

AbstractBackgroundPlasma phosphorylated tau (p‐tau)231 is an emerging blood‐based biomarker that has shown great promise for early identification of individuals at risk of Alzheimer’s disease (AD). However, it is unclear if this biomarker is sufficient as a standalone test or if diagnostic classification improves when simultaneously considering other plasma biomarkers. We investigated various plasma biomarkers, individually and in combination, in relation to cerebrospinal fluid (CSF) amyloid positivity.MethodVanderbilt Memory and Aging Project participants free of clinical dementia and stroke (n = 155, 72±6 years, 33% female) underwent fasting lumbar puncture and blood draw. CSF β‐amyloid42 (Aβ42) and β‐amyloid40 (Ab40) and plasma p‐tau231, glial fibrillary acidic protein (GFAP), Aβ42, Aβ40, and neurofilament light chain (NfL) were analyzed in batch. The Aβ42/40 ratio was used for all analyses and CSF amyloid positivity was defined as Aβ42/40 ratio &lt;0.072. Logistic regression models related each biomarker to amyloid positivity, adjusting for age, sex, race/ethnicity, apolipoprotein E‐ε4 status, and cognitive status. Subsequent models included p‐tau231 as the predictor in a base model with other biomarkers being added individually and then in combination with other biomarkers to the base model. Models were compared using likelihood ratio test (LRTest) and Akaike Information Criterion (AIC) values.ResultIn single predictor models, p‐tau231 (C‐index = 0.87, p = 0.005) and GFAP (C‐index = 0.87, p = 0.01) were associated with amyloid positivity, while other biomarkers were not (p‐values&gt;0.35). In models including p‐tau231 and various biomarker combinations, p‐tau231 remained significant in each model (p‐values&lt;0.02). In comparison to the base model with p‐tau231 only, models adding GFAP (AIC = 143.8, LRTest p = 0.008), GFAP and NfL (AIC = 145.5, LRTest p = 0.02), and GFAP and Aβ42/40 (AIC = 145.8, LRTest p = 0.03) improved prediction of amyloid positivity. The model including plasma p‐tau231 and GFAP was the best fitting model (C‐index = 0.89).ConclusionIn this sample of community‐dwelling older adults free of clinical dementia, we found plasma p‐tau231 and GFAP were associated with amyloid positivity. Models including both p‐tau231 and GFAP performed best, and adding additional plasma biomarkers to this combination did not produce a better fitting model. Taken together, these findings highlight the utility of plasma p‐tau231 for screening for amyloid status and suggest that including plasma GFAP will improve discriminative accuracy.

Cluster analysis of the longitudinal progression of plasma biomarkers and amyloid imaging in the Wisconsin Registry for Alzheimer’s Prevention

AbstractBackgroundThe analysis of the longitudinal progression of plasma biomarkers and amyloid imaging might lead to new insights in the early pattern of biomarker changes.MethodPlasma Aß42/Aß40, pTau231/Aß42, pTau181/Aß42, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NFL) were measured for n = 223 subjects and 1673 total visits in the Wisconsin Registry for Alzheimer’s Prevention (WRAP) using Quanterix Simoa assays at the University of Gothenburg. Global distribution volume ratio (DVR) was computed from 11C‐Pittsburgh compound B PET scans. Inclusion criteria included Amyloid or CSF Amyloid positive status, and at least one plasma serial value. Trajectories spanning age 46.9 to 80.3 years old for the DVR and plasma biomarkers were estimated by fitting a vector field to the longitudinal data (https://arxiv.org/abs/2206.15215). A Louvain community clustering analysis was performed. The time from Amyloid positivity (DVR = 1.19; centiloid of 22) was also computed for all individual trajectories that crossed this threshold.ResultThe Louvain community clustering algorithm resulted in three distinct communities (Fig 1): no increase in DVR or plasma biomarkers (Green, n = 88(39.5%)); increase in DVR and all plasma biomarkers except NFL (Red, 41(18.4%)); and mixed DVR plus GFAP and NFL increase (Blue, 94(42.2%)). Time since Amyloid positivity (TSAP) was computed for the subset of subjects (n = 84) expected to cross the Amyloid positivity threshold. The Louvain communities as a function of TSAP are shown in Fig 2.ConclusionWe identified three communities regarding the longitudinal progression of amyloid PET and plasma biomarker levels. Plasma biomarkers were dynamic up to 30 years before Amyloid positivity.

Cluster analysis of the longitudinal progression of plasma biomarkers and amyloid imaging in the Wisconsin Registry for Alzheimer’s Prevention

AbstractBackgroundThe analysis of the longitudinal progression of plasma biomarkers and amyloid imaging might lead to new insights in the early pattern of biomarker changes.MethodPlasma Aβ42/Aβ40, pTau231/Aβ42, pTau181/Aβ42, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NFL) were measured for n = 223 subjects and 1673 total visits in the Wisconsin Registry for Alzheimer’s Prevention (WRAP) using Quanterix Simoa assays at the University of Gothenburg. Global distribution volume ratio (DVR) was computed from 11C‐Pittsburgh compound B PET scans. Inclusion criteria included Amyloid or CSF Amyloid positive status, and at least one plasma serial value. Trajectories spanning age 46.9 to 80.3 years old for the DVR and plasma biomarkers were estimated by fitting a vector field to the longitudinal data (https://arxiv.org/abs/2206.15215). A Louvain community clustering analysis was performed. The time from Amyloid positivity (DVR = 1.19; centiloid of 22) was also computed for all individual trajectories that crossed this threshold.ResultThe Louvain community clustering algorithm resulted in three distinct communities (Fig 1): no increase in DVR or plasma biomarkers (Green, n = 88(39.5%)); increase in DVR and all plasma biomarkers except NFL (Red, 41(18.4%)); and mixed DVR plus GFAP and NFL increase (Blue, 94(42.2%)). Time since Amyloid positivity (TSAP) was computed for the subset of subjects (n = 84) expected to cross the Amyloid positivity threshold. The Louvain communities as a function of TSAP are shown in Fig 2.ConclusionWe identified three communities regarding the longitudinal progression of amyloid PET and plasma biomarker levels. Plasma biomarkers were dynamic up to 30 years before Amyloid positivity.

Subjective cognitive impairment and biomarkers of Alzheimer’s disease

AbstractBackgroundSubjective cognitive impairment (SCI) is common in older adults. It may represent the earliest clinical manifestation of cognitive decline and Alzheimer’s disease (AD).Objectives1) to evaluate associations between SCI and cerebrospinal fluid (CSF) biomarkers of AD; 2) to identify specific single questions or question combinations that are related to the presence of cerebral AD pathology; 3) to evaluate whether using SCI related scores or specific questions combined with other clinical features and biomarkers may improve prediction of cognitive decline.MethodWe conducted a longitudinal study in 164 non‐demented subjects participating on a biomarker study in a memory clinic setting, aged 53 to 88 years, with CDR scores of 0 (N = 84) and 0.5 (N = 80). SCI was assessed using the Cognitive Complaint Interview (CCI, Thomas‐Antérion et al., 2006), a validated 10 items questionnaire. Accordingly, SCI is defined as present when the subject answers “yes” to 3 or more items; and/or to item 5, and/or to items A, 4, 5, 7, 8. All participants underwent comprehensive neuropsychological evaluations and CSF biomarkers analysis. “Amyloid positivity" and “AD pathology” were defined according to the CSF Aβ1‐42 levels and pTau181/Aβ1‐42 ratio, respectively. Logistic regressions were used to address the associations of interest.ResultsParticipants with SCI had significantly lower Aβ1‐42 and higher Tau and pTau181 levels compared to those without SCI. SCI was associated with amyloid positivity while CCI total score and the question A (reporting changes in memory) were associated with both amyloid positivity and AD pathology in the whole cohort. When performing analysis separately in the CDR = 0 and CDR = 0.5 subgroups the associations remained significant in the CDR = 0.5 group. Only question 6 (difficulties in finding words) was associated with AD pathology in the CDR = 0 group. Results on the prediction of cognitive decline at follow‐up visits will be presented at the conference.ConclusionIn dementia‐free subjects from this memory clinic cohort SCI‐related scores and single questions indicate amyloid positivity and the presence of AD pathology depending on the absence/presence of mild cognitive impairment. Further studies are needed to evaluate the diagnostic and prognostic utility of cognitive complains in memory clinic patients.

Discordance in amyloid positivity defined by Visual Reads VR and Centiloids CL

AbstractBackgroundAmyloid PET VR is the most common method to determine Aβ pathology in clinical practice. Amyloid positivity by VR or CL cut‐off generally shows a good concordance, however, it is possible to have discordant cases whereby the VR is positive, but CL is below the cut‐off of amyloid positivity, or vice versa. The objective of this analysis was to assess the rate and cause of discordance in defining amyloid pathology using VR and CL in Eisai’s Elenbecestat MissionAD Phase 3 program.MethodFlorbetaben, Florbetapir, and Flutemetamol amyloid PET scans from 3412 participants with MCI due to AD or mild AD dementia were visually read at screening by trained neuroradiologists and quantitated using CLs. VR classification of the amyloid status is based on the higher or equal grey matter (GM) uptake relative to uptake in the white matter (WM) in at least one area, as per manufacturer’s guidance. Quantitatively, amyloid negativity was defined as CL&lt;30. The number of positive cortical regions (SUVR&gt;1.17) was also calculated.ResultThe positivity concordance between VR and CL was 92%, while the negativity concordance was 94.6%. VR+/CL‐ discordance was 8% (n = 1814 VR+, n = 145 CL‐) whereas VR‐/CL+ discordance was 5.4% (n = 1598 VR‐, n = 86 CL+). VR+/CL‐ discordant cases had significantly fewer positive cortical regions than both VR+/CL+ and VR‐/CL+ cases (Figure 1). VR‐/CL+ had a significantly higher WM uptake than both VR‐/CL‐ and VR+/CL‐ cases (Figure 2).ConclusionVR+/CL‐ discordant cases show fewer amyloid positive cortical regions, computing CLs results in below cut‐off values due to a dilution effect. These cases are considered VR+ as per manufacturer’s guidelines as they show at least one area of amyloid accumulation. On the other hand, VR‐/CL+ discordant cases result from an increased WM uptake, reducing GM/WM contrast required to determine visual read positivity. VR represents a robust and validated method to determine the presence of amyloid deposition and enables enrolling patients with amyloid beta pathology, as visible on amyloid PET scans, while CLs are optimal for assessing longitudinal changes over time and are a more sensitive measure to assess disease progression.

Clinical and neuroimaging predictors of Alzheimer’s Dementia Conversion in patients with mild cognitive impairment using 18F‐Florapronol amyloid PET by quantitative analysis over 2 years

AbstractBackgroundPatients with mild cognitive impairment(MCI) are a clinically important group because of the increased risk for the progression to Alzheimer’s dementia(AD). Amyloid positron emission tomography(PET) was useful as a predictor for identifying AD pathology in the pre‐dementia stage. We determined the rates of conversion to AD in MCI patients based on amyloid positivity by visual and quantitative analysis over 2 years. We also investigated the clinical and neuroimaging predictors for conversion to AD.MethodIndividuals aged 50 and above with MCI according to Petersen’s criteria were eligible. Subjects underwent laboratory tests including APOE genotyping, neuropsychological tests, brain magnetic resonance imaging(MRI), and amyloid PET assessed by 18F‐FC119S. Amyloid positivity was determined in each lobe of the brain and quantitatively analyzed. The follow‐up period was at least 2 years. Cox regression analysis was performed to identify prognostic factors which are independently related to time to AD conversion.ResultWe recruited 50 patients and 39 subjects were enrolled. 5 patients dropped out at 2 years, so 34 subjects were finally included. 13 of 34(38.2%) by visual analysis and 12 of 34(35.3%) by quantitative analysis of 18F‐FC119S PET showed amyloid positive. The basic demographics were not significantly different between the groups except for Korea‐Instrumental Activities of Daily Living(K‐IADL) at baseline(p = 0.004) and 1 year(p = 0.001), Mini‐mental State Examination(MMSE) at 2 years(p = 0.047) and Global Deterioration Scale(GDS) at 2 years(p = 0.029). 4 of 13(30.8%) by visual analysis, 4 of 12(66.7%) by quantitative analysis amyloid‐positive MCI patients converted to AD. Amyloid positive in posterior cingulate was marginally significant(p = 0.066). AD conversion group had a higher Clinical Dementia Rating‐Sum of box(CDR‐SOB) at baseline(p = 0.002) compared with remained MCI group. In multivariate cox regression, CDR‐SOB at baseline, MMSE at 1 year and K‐IADL at 2 years were included.ConclusionAmyloid‐positive MCI group was more likely to progress to AD. But long‐term follow‐up was required. By quantitative analysis of 18F‐FC119S PET, amyloid deposition in posterior cingulate was highly associated with the AD conversion group. CDR‐SOB at baseline, MMSE at 1 year and K‐IADL at 2 years were considered as clinically valuable prognostic predictors of disease progression. Especially, CDR‐SOB is important as a predictive biomarker in the pre‐dementia stage.