Aisen, Paul

Abstract In vivo gold standard for the ante-mortem assessment of brain β-amyloid pathology is currently β-amyloid positron emission tomography or cerebrospinal fluid measures of β-amyloid42 or the β-amyloid42/β-amyloid40 ratio. The widespread acceptance of a biomarker classification scheme for the Alzheimer’s disease continuum has ignited interest in more affordable and accessible approaches to detect Alzheimer’s disease β-amyloid pathology, a process that often slows down the recruitment into, and adds to the cost of, clinical trials. Recently, there has been considerable excitement concerning the value of blood biomarkers. Leveraging multidisciplinary data from cognitively unimpaired participants and participants with mild cognitive impairment recruited by the multisite biomarker study of Alzheimer’s Disease Neuroimaging Initiative, here we assessed to what extent plasma β-amyloid42/β-amyloid40, neurofilament light and phosphorylated-tau at threonine-181 biomarkers detect the presence of β-amyloid pathology, and to what extent the addition of clinical information such as demographic data, APOE genotype, cognitive assessments and MRI can assist plasma biomarkers in detecting β-amyloid-positivity. Our results confirm plasma β-amyloid42/β-amyloid40 as a robust biomarker of brain β-amyloid-positivity (area under curve, 0.80–0.87). Plasma phosphorylated-tau at threonine-181 detected β-amyloid-positivity only in the cognitively impaired with a moderate area under curve of 0.67, whereas plasma neurofilament light did not detect β-amyloid-positivity in either group of participants. Clinical information as well as MRI-score independently detected positron emission tomography β-amyloid-positivity in both cognitively unimpaired and impaired (area under curve, 0.69–0.81). Clinical information, particularly APOE ε4 status, enhanced the performance of plasma biomarkers in the detection of positron emission tomography β-amyloid-positivity by 0.06–0.14 units of area under curve for cognitively unimpaired, and by 0.21–0.25 units for cognitively impaired; and further enhancement of these models with an MRI-score of β-amyloid-positivity yielded an additional improvement of 0.04–0.11 units of area under curve for cognitively unimpaired and 0.05–0.09 units for cognitively impaired. Taken together, these multi-disciplinary results suggest that when combined with clinical information, plasma phosphorylated-tau at threonine-181 and neurofilament light biomarkers, and an MRI-score could effectively identify β-amyloid+ cognitively unimpaired and impaired (area under curve, 0.80–0.90). Yet, when the MRI-score is considered in combination with clinical information, plasma phosphorylated-tau at threonine-181 and plasma neurofilament light have minimal added value for detecting β-amyloid-positivity. Our systematic comparison of β-amyloid-positivity detection models identified effective combinations of demographics, APOE, global cognition, MRI and plasma biomarkers. Promising minimally invasive and low-cost predictors such as plasma biomarkers of β-amyloid42/β-amyloid40 may be improved by age and APOE genotype.

AbstractBackgroundApproximately 5% of the 5.6 million (∼280,000) Americans with Alzheimer’s disease (AD) develop symptoms at age 65 or younger and are classified as having early‐onset AD (EOAD). Although EOAD and late‐onset AD (LOAD) share the same pathologic substrate, there are notable differences in their clinical and biological phenotypes. The Longitudinal Early‐onset AD study (LEADS) is a multi‐site, observational clinical and biomarker study of EOAD. In the present study we utilized the currently available LEADS imaging data to explore the spatial extent and magnitude of neurodegeneration EOADMethod95 amyloid positive patients with EOAD, 37 amyloid negative cognitively impaired patients (EOnonAD) , and 55 cognitively unimpaired controls (CNs) were included in the analysis. The magnitude of cortical thinning in the EOAD and EOnonAD groups was estimated using whole‐brain GLMs compared to the CN group. In addition, w‐scores were derived for each EOAD patient for these ROIs: average cortical thickness, hippocampal volume, AD‐signature cortical thickness (typically used in LOAD), and precuneus cortical thickness controlling for age, sex, and acquisition site. The utility of each ROI to differentiate EOAD patients from CNs was assessed by calculating the percentage of patients falling below a W‐threshold of ‐1.5.ResultIn EOAD, a distributed pattern of cortical thinning was observed in lateral temporal, parietal, and frontal cortex (p<.01), and was most prominent in the precuneus (Cohen’s d’ = 1.5). In the EOnonAD group, cortical thinning was observed but was highly variable across individuals. The w‐score analysis in the EOAD group yielded the following results: Mean Cortical thickness: Mean = ‐3.34, SD = 2.97, Classification = 72%.; Hippocampal volume: Mean = ‐2.96, SD = 2.39, Classification = 74%; AD Signature: Mean = ‐4.63, SD = 3.07, Classification = 80%; Precuneus: Mean = ‐5.98, SD = 4.1, Classification = 90%.ConclusionOur results are consistent with previous findings demonstrating cortical thinning in lateral parietal, precuneus, and lateral temporal lobes in EOAD with relatively less involvement of the medial temporal lobes. Our results also suggest that the precuneus may be especially vulnerable to neurodegeneration in patients with EOAD and may serve as a useful biomarker for patient classification.

AbstractBackgroundThe NIA‐AA Research Framework on Alzheimer’s disease (AD) represents an important advance in the biological characterization of the AD spectrum. While this framework predicts disease progression, definitions of amyloid, tau, and neurodegeneration positivity based on cut‐scores may limit the performance of predictive models.Method252 cognitively normal (CN), 424 amnestic mild cognitive impairment (aMCI) and 128 AD participants were included in this study. aMCI participants at baseline were classified using 2 different methods: 1) Using conventional AT[N] criteria based on proposed cut‐offs in CSF or brain imaging measures: participants were classified on the AD continuum (A+T‐N‐, A+T+N‐, A+T‐N+, A+T+N+) and non‐AD continuum (all other subgroups); and 2) using a machine learning (ML) based method (ensemble linear discriminant model built using CN and AD participants) to classify MCI participants as CN‐like or AD‐like based on continuous CSF biomarkers, regional amyloid PET, FDG‐PET, and volumetric MRI (variables contributing to AT[N] classification; Table 1). Predictive performance of AT[N]‐based classification and ML‐based classification were assessed using clinical outcomes at 0.5, 1, 2, 3, 4, and 5 years of follow‐up.ResultA total of 414 aMCI participants had at least one wave of follow‐up (N of 397, 400, 346, 297, 238, and 93 at 0.5, 1, 2, 3, 4, and 5 years, respectively). The progression rate from aMCI to AD was 5.8% at 6m, 9.5% at 1y, 17.9% at 2y, 19.9% at 3y, 20.2% at 4y, and 15% at 5y of follow‐up. Presence of AD pathology at baseline (AT[N]‐based AD continuum) predicted progression to AD with high sensitivity but modest specificity during follow up (Table 2). ML‐based classification was less sensitive but substantially more specific. ML‐based classification models were significantly more accurate than AT[N]‐based classification at all follow‐up periods (McNemar test, p‐value<0.001 for all).ConclusionA single cut‐point approach used in AT[N] classification lacks accuracy when research questions require high diagnostic or prognostic certainty. A multivariate approach using ML methods can improve accuracy of prediction of disease progression. PPVs were higher at all follow‐up time points for ML‐based classification, indicating that progression to AD occurs at a higher rate in persons identified by ML.

AbstractBackgroundPrevious studies have reported that age modifies the distribution and burden of tau (and, to a lesser extent, amyloid) pathology in sporadic Alzheimer’s disease (AD). Here we present preliminary baseline amyloid and tau PET results from the Longitudinal Early‐Onset Alzheimer’s Disease Study (LEADS), a multi‐site longitudinal study of sporadic early‐onset AD.Method135 patients meeting clinical criteria for MCI or probable AD and 50 cognitively normal controls (all age<65 at enrollment) were enrolled at 12 US centers between August 2018 and December 2019 (Table 1). 18F‐Florbetaben amyloid‐PET (FBB) was used to assign patients to EOAD (amyloid‐positive) or EOnonAD (amyloid‐negative) subgroups based on visual rating and semi‐quantification. 130 patients and all controls had 18F‐Flortaucipir tau‐PET (FTP). Regional Standardized Uptake Value Ratios (SUVR) for FBB (whole cerebellum reference) and FTP (inferior cerebellar gray reference) were extracted using co‐registered 3T‐MRI.Result98 patients (72.6%) were amyloid PET‐positive (EOAD) and 37 (27.4%) were amyloid PET‐negative (EOnonAD). Compared to EOAD, EOnonAD patients had higher MMSE, MOCA and CDR sum‐of‐boxes (CDR‐SB) and were more frequently male (Table 1). Patients with EOAD showed elevated FBB and FTP SUVR in temporoparietal and frontal cortex compared to CN and EOnonAD (Figures 1‐2). In EOAD, MMSE, MOCA and CDR‐SB were significantly correlated with FTP SUVR (Figure 3), while no significant correlations were found with FBB SUVR. EOnonAD patients showed variable FTP binding ranging from negative to mildly elevated binding in anterior temporal and frontal cortex and underlying white matter. Two EOnonAD cases showed intense FTP binding comparable to typical EOAD cases, despite visually and quantitatively negative FBB scans.ConclusionPatients with clinically mild, sporadic EOAD typically show an extensive distribution and burden of tau pathology in the setting of positive amyloid PET. Global clinical measures correlate with tau but not amyloid PET. Over 25% of patients meeting clinical criteria for early‐onset MCI/probable AD have negative amyloid PET, suggesting alternative etiologies for cognitive decline. These findings will inform future design of drug trials in this important and under‐studied population.

AbstractIntroductionWe conducted Japanese Alzheimer's Disease Neuroimaging Initiative (J‐ADNI) and compared the basic characteristics and progression profiles with those of ADNI in North America.MethodsA total of 537 Japanese subjects with normal cognition, late amnestic mild cognitive impairment (LMCI), or mild Alzheimer's disease (AD) were enrolled using the same criteria as ADNI. Rates of changes in representative cognitive or functional measures were compared for amyloid positron emission tomography‐ or cerebrospinal fluid amyloid β(1–42)‐positive LMCI and mild AD between J‐ADNI and ADNI.ResultsAmyloid positivity rates were significantly higher in normal cognition of ADNI but at similar levels in LMCI and mild AD between J‐ADNI and ADNI. Profiles of decline in cognitive or functional measures in amyloid‐positive LMCI in J‐ADNI (n = 75) and ADNI (n = 269) were remarkably similar, whereas those in mild AD were milder in J‐ADNI (n = 73) compared with ADNI (n = 230).DiscussionThese results support the feasibility of bridging of clinical trials in the prodromal stage of AD between Asia and western countries.