Longitudinal Trajectories of the Cognitive Function Index in the A4 Study.

BACKGROUND: The Anti-Amyloid in Asymptomatic Alzheimers Disease (A4) Study failed to show a treatment benefit with solanezumab, but the longitudinal consequences of elevated amyloid were observed in study participants with objective decline on the Preclinical Alzheimer Cognitive Composite (PACC) and subjective decline on the combined Cognitive Function Index (participant + study partner CFI), during the trial period. OBJECTIVES: We sought to expand on previous findings by comparing longitudinal patterns of participant and study partner CFI separately and their associations with the PACC stratified by baseline amyloid tertile over the course of the A4 Study. DESIGN: Cognitively unimpaired older adult participants and their study partners were independently administered the CFI at screen prior to amyloid PET disclosure and then at 3 subsequent visits (week 48, week 168, week 240) of the study. PACC collected at visits concurrent with CFI administration were also examined longitudinally. SETTING: The A4 Study was conducted at 67 sites in Australia, Canada, Japan, and the United States. PARTICIPANTS: 1,147 participants with elevated amyloid based on florbetapir PET were enrolled in the A4 Study and included in these analyses. 583 were on placebo and 564 were treated with solanezumab. MEASUREMENTS: The PACC was used to assess objective cognitive performance and the CFI was used to assess change in everyday cognitive functioning by the participant and their study partner independently. Amyloid level was characterized by Centiloid tertiles (<46.1 CL, 46.1 to 77.2 CL, >77.2 CL). Participants were aware of their elevated amyloid status, but not their CL tertile, or specific level of amyloid. Longitudinal correlations between participant and study partner CFI and PACC were examined at all visits where assessments were available. The impact of baseline amyloid tertile on CFI and PACC associations was also examined. RESULTS: Both participant and study partner CFI increased over the duration of the study indicating worsening cognitive functioning. Results did not differ by treatment group. The association between higher CFI and worse PACC for both for participant and study partner became progressively stronger over the course of the study. PACC had a significantly higher correlation with study partner CFI than with participant CFI by week 168. The stronger correlations between study partner CFI and PACC were driven by those in the highest amyloid tertile. CONCLUSION: Both participant and study partner report captured subtle changes in everyday cognitive functioning for participants with biomarker confirmed and disclosed preclinical AD. Moreover, study partner report was most highly aligned with cognitive decline, particularly among those with the highest amyloid load

The BIN1 rs744373 SNP is associated with increased tau-PET levels and impaired memory

© 2019, The Author(s). The single nucleotide polymorphism (SNP) rs744373 in the bridging integrator-1 gene (BIN1) is a risk factor for Alzheimer’s disease (AD). In the brain, BIN1 is involved in endocytosis and sustaining cytoskeleton integrity. Post-mortem and in vitro studies suggest that BIN1-associated AD risk is mediated by increased tau pathology but whether rs744373 is associated with increased tau pathology in vivo is unknown. Here we find in 89 older individuals without dementia, that BIN1 rs744373 risk-allele carriers show higher AV1451 tau-PET across brain regions corresponding to Braak stages II–VI. In contrast, the BIN1 rs744373 SNP was not associated with AV45 amyloid-PET uptake. Furthermore, the rs744373 risk-allele was associated with worse memory performance, mediated by increased global tau levels. Together, our findings suggest that the BIN1 rs744373 SNP is associated with increased tau but not beta-amyloid pathology, suggesting that alterations in BIN1 may contribute to memory deficits via increased tau pathology

Challenges and Opportunities with Causal Discovery Algorithms: Application to Alzheimer’s Pathophysiology

© 2020, The Author(s). Causal Structure Discovery (CSD) is the problem of identifying causal relationships from large quantities of data through computational methods. With the limited ability of traditional association-based computational methods to discover causal relationships, CSD methodologies are gaining popularity. The goal of the study was to systematically examine whether (i) CSD methods can discover the known causal relationships from observational clinical data and (ii) to offer guidance to accurately discover known causal relationships. We used Alzheimer’s disease (AD), a complex progressive disease, as a model because the well-established evidence provides a “gold-standard” causal graph for evaluation. We evaluated two CSD methods, Fast Causal Inference (FCI) and Fast Greedy Equivalence Search (FGES) in their ability to discover this structure from data collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used structural equation models (which is not designed for CSD) as control. We applied these methods under three scenarios defined by increasing amounts of background knowledge provided to the methods. The methods were evaluated by comparing the resulting causal relationships with the “gold standard” graph that was constructed from literature. Dedicated CSD methods managed to discover graphs that nearly coincided with the gold standard. For best results, CSD algorithms should be used with longitudinal data providing as much prior knowledge as possible

Pre-Randomization Predictors of Study Discontinuation in a Preclinical Alzheimers Disease Randomized Controlled Trial.

BACKGROUND: Participant discontinuation from study treatment in a clinical trial can leave a trial underpowered, produce bias in statistical analysis, and limit interpretability of study results. Retaining participants in clinical trials for the full study duration is therefore as important as participant recruitment. OBJECTIVE: This analysis aims to identify associations of pre-randomization characteristics of participants with premature discontinuation during the blinded phase of the Anti-Amyloid treatment in Asymptomatic AD (A4) Study. DESIGN: All A4 trial randomized participants were classified as having prematurely discontinued study during the blinded period of the study for any reason (dropouts) or completed the blinded phase of the study on treatment (completers). SETTING: The trial was conducted across 67 study sites in the United States, Canada, Japan and Australia through the global COVID-19 pandemic. PARTICIPANTS: The sample consisted of all 1169 A4 trial randomized participants. MEASUREMENTS: Pre-randomization demographic, clinical, amyloid PET and genetic predictors of study discontinuation were evaluated using a univariate generalized linear mixed model (GLMM), with discontinuation status as the binary outcome, each predictor as a fixed effect, and site as a random effect to account for differences among study sites in the trial. Characteristics significant at p<0.10 were then included in a multivariable GLMM. RESULTS: Among randomized participants, 339 (29%) discontinued the study during the blinded period (median follow-up time in trial: 759 days). From the multivariable analysis, the two main predictors of study discontinuation were screening State-Trait Anxiety Inventory (STAI) scores (OR = 1.07 [95%CI = 1.02; 1.12]; p=0.002) and age (OR = 1.06 [95%CI = 1.03; 1.09]; p<0.001). Participants with a family history of dementia (OR = 0.75 [95%CI = 0.55; 1.01]; p=0.063) and APOE ε4 carriers (OR = 0.79 [95%CI = 0.6; 1.04]; p=0.094) were less likely to discontinue from the study, with the association being marginally significant. In these analyses, sex, race and ethnicity, cognitive scores and amyloid/tau PET scores were not associated with study dropout. CONCLUSIONS: In the A4 trial, older participants and those with higher levels of anxiety at baseline as measured by the STAI were more likely to discontinue while those who had a family history of dementia or were APOE ε4 carriers were less likely to drop out. These findings have direct implications for future preclinical trial design and selection processes to identify those individuals at greatest risk of dropout and provide information to the study team to develop effective selection and retention strategies in AD prevention studies

The case for low-level BACE1 inhibition for the prevention of Alzheimer disease

Alzheimer disease (AD) is the most common cause of dementia in older individuals (>65 years) and has a long presymptomatic phase. Preventive therapies for AD are not yet available, and potential disease-modifying therapies targeting amyloid-β plaques in symptomatic stages of AD have only just been approved in the United States. Small-molecule inhibitors of β-site amyloid precursor protein (APP)-cleaving enzyme 1 (BACE1; also known as β-secretase 1) reduce the production of amyloid-β peptide and are among the most advanced drug candidates for AD. However, to date all phase II and phase III clinical trials of BACE inhibitors were either concluded without benefit or discontinued owing to futility or the occurrence of adverse effects. Adverse effects included early, mild cognitive impairment that was associated with all but one inhibitor; preliminary results suggest that the cognitive effects are non-progressive and reversible. These discontinuations have raised questions regarding the suitability of BACE1 as a drug target for AD. In this Perspective, we discuss the status of BACE inhibitors and suggest ways in which the results of the discontinued trials can inform the development of future clinical trials of BACE inhibitors and related secretase modulators as preventative therapies. We also propose a series of experiments that should be performed to inform ‘go–no-go’ decisions in future trials with BACE inhibitors and consider the possibility that low levels of BACE1 inhibition could avoid adverse effects while achieving efficacy for AD prevention

Amyloid and Tau Prediction of Cognitive and Functional Decline in Unimpaired Older Individuals: Longitudinal Data from the A4 and LEARN Studies.

BACKGROUND: Converging evidence suggests that markers of Alzheimers disease (AD) pathology in cognitively unimpaired older individuals are associated with high risk of cognitive decline and progression to functional impairment. The Anti-Amyloid Treatment in Asymptomatic Alzheimers disease (A4) and Longitudinal Evaluation of Amyloid and Neurodegeneration Risk (LEARN) Studies enrolled a large cohort of cognitively normal older individuals across a range of baseline amyloid PET levels. Recent advances in AD blood-based biomarkers further enable the comparison of baseline markers in the prediction of longitudinal clinical outcomes. OBJECTIVES: We sought to evaluate whether biomarker indicators of higher levels of AD pathology at baseline predicted greater cognitive and functional decline, and to compare the relative predictive power of amyloid PET imaging, tau PET imaging, and a plasma P-tau217 assay. DESIGN: All participants underwent baseline amyloid PET scan, plasma P-tau217; longitudinal cognitive testing with the Primary Alzheimer Cognitive Composite (PACC) every 6 months; and annual functional assessments with the clinical dementia rating (CDR), cognitive functional index (CFI), and activities of daily living (ADL) scales. Baseline tau PET scans were obtained in a subset of participants. Participants with elevated amyloid (Aβ+) on screening PET who met inclusion/exclusion criteria were randomized to receive placebo or solanezumab in a double-blind phase of the A4 Study over 240+ weeks. Participants who did not have elevated amyloid (Aβ-) but were otherwise eligible for the A4 Study were referred to the companion observational LEARN Study with the same outcome assessments over 240+ weeks. SETTING: The A4 and LEARN Studies were conducted at 67 clinical trial sites in the United States, Canada, Japan and Australia. PARTICIPANTS: Older participants (ages 65-85) who were cognitively unimpaired at baseline (CDR-GS=0, MMSE 25-30 with educational adjustment, and Logical Memory scores within the normal range LMIIa 6-18) were eligible to continue in screening. Aβ+ participants were randomized to either placebo (n=583) or solanezumab (n=564) in the A4 Study. A subset of Aβ+ underwent tau PET imaging in A4 (n=350). Aβ- were enrolled into the LEARN Study (n=553). MEASUREMENTS: Baseline 18-F Florbetapir amyloid PET, 18-F Flortaucipir tau PET in a subset and plasma P-tau217 with an electrochemiluminescence (ECL) immunoassay were evaluated as predictors of cognitive (PACC), and functional (CDR, CFI and ADL) change. Models were evaluated to explore the impact of baseline tertiles of amyloid PET and tertiles of plasma P-tau217 on cognitive and functional outcomes in the A4 Study compared to LEARN. Multivariable models were used to evaluate the unique and common variance explained in longitudinal outcomes based on baseline predictors, including effects for age, gender, education, race/ethnic group, APOEε4 carrier status, baseline PACC performance and treatment assignment in A4 participants (solanezumab vs placebo). RESULTS: Higher baseline amyloid PET CL and P-tau217 levels were associated with faster rates of PACC decline, and increased likelihood of progression to functional impairment (CDR 0.5 or higher on two consecutive measurements), both across LEARN Aβ- and A4 Aβ+ (solanezumab and placebo arms). In analyses considering all baseline predictor variables, P-tau217 was the strongest predictor of PACC decline. Among participants in the highest tertiles of amyloid PET or P-tau217, >50% progressed to CDR 0.5 or greater. In the tau PET substudy, neocortical tau was the strongest predictor of PACC decline, but plasma P-tau217 contributed additional independent predictive variance in commonality variance models. CONCLUSIONS: In a large cohort of cognitively unimpaired individuals enrolled in a Phase 3 clinical trial and companion observational study, these findings confirm that higher baseline levels of amyloid and tau markers are associated with increased rates of cognitive decline and progression to functional impairment. Interestingly, plasma P-tau217 was the best predictor of decline in the overall sample, superior to baseline amyloid PET. Neocortical tau was the strongest predictor of cognitive decline in the subgroup with tau PET, suggesting that tau deposition is most closely linked to clinical decline. These findings indicate that biomarkers of AD pathology are useful to predict decline in an older asymptomatic population and may prove valuable in the selection of individuals for disease-modifying treatments

Drug development in Alzheimer’s disease: The path to 2025

The global impact of Alzheimer’s disease (AD) continues to increase, and focused efforts are needed to address this immense public health challenge. National leaders have set a goal to prevent or effectively treat AD by 2025. In this paper, we discuss the path to 2025, and what is feasible in this time frame given the realities and challenges of AD drug development, with a focus on disease-modifying therapies (DMTs). Under the current conditions, only drugs currently in late Phase 1 or later will have a chance of being approved by 2025. If pipeline attrition rates remain high, only a few compounds at best will meet this time frame. There is an opportunity to reduce the time and risk of AD drug development through an improvement in trial design; better trial infrastructure; disease registries of well-characterized participant cohorts to help with more rapid enrollment of appropriate study populations; validated biomarkers to better detect disease, determine risk and monitor disease progression as well as predict disease response; more sensitive clinical assessment tools; and faster regulatory review. To implement change requires efforts to build awareness, educate and foster engagement; increase funding for both basic and clinical research; reduce fragmented environments and systems; increase learning from successes and failures; promote data standardization and increase wider data sharing; understand AD at the basic biology level; and rapidly translate new knowledge into clinical development. Improved mechanistic understanding of disease onset and progression is central to more efficient AD drug development and will lead to improved therapeutic approaches and targets. The opportunity for more than a few new therapies by 2025 is small. Accelerating research and clinical development efforts and bringing DMTs to market sooner would have a significant impact on the future societal burden of AD. As these steps are put in place and plans come to fruition, e.g., approval of a DMT, it can be predicted that momentum will build, the process will be self-sustaining, and the path to 2025, and beyond, becomes clearer

Serial nuclear magnetic resonance imaging in acute myocardial infarction

Recent studies show that proton nuclear magnetic resonance (NMR) imaging can detect myocardial ischemia and acute myocardial infarction (AMI) in animal models1-3 and in humans.4-6 The area of AMI appears as increased signal intensity on the spin-echo NMR images and most likely reflects the regional edema associated with tissue necrosis.1 Thus, the time course of regional edema and the evolution of infarct healing may be revealed by serial NMR studies. In a recent canine study, Pflugfelder et al7 examined the time course of the increased NMR signal intensity associated with AMI. They found that the relative signal intensity increased between the day of AMI and 2 weeks after AMI and subsequently decreased by the 20th day. We examined the early time course of NMR changes in humans.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26729/1/0000279.pd

BRISC—An Open Source Pulmonary Nodule Image Retrieval Framework

We have created a content-based image retrieval framework for computed tomography images of pulmonary nodules. When presented with a nodule image, the system retrieves images of similar nodules from a collection prepared by the Lung Image Database Consortium (LIDC). The system (1) extracts images of individual nodules from the LIDC collection based on LIDC expert annotations, (2) stores the extracted data in a flat XML database, (3) calculates a set of quantitative descriptors for each nodule that provide a high-level characterization of its texture, and (4) uses various measures to determine the similarity of two nodules and perform queries on a selected query nodule. Using our framework, we compared three feature extraction methods: Haralick co-occurrence, Gabor filters, and Markov random fields. Gabor and Markov descriptors perform better at retrieving similar nodules than do Haralick co-occurrence techniques, with best retrieval precisions in excess of 88%. Because the software we have developed and the reference images are both open source and publicly available they may be incorporated into both commercial and academic imaging workstations and extended by others in their research

Effects of traumatic brain injury and posttraumatic stress disorder on development of Alzheimer's disease in Vietnam Veterans using the Alzheimer's Disease Neuroimaging Initiative: Preliminary report

Introduction Traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) have previously been reported to be associated with increased risk of Alzheimer's disease (AD). We are using biomarkers to study Vietnam Veterans with/without mild cognitive impairment with a history of at least one TBI and/or ongoing PTSD to determine whether these contribute to the development of AD. Methods Potential subjects identified by Veterans Administration records underwent an initial telephone screen. Consented subjects underwent clinical evaluation, lumbar puncture, structural magnetic resonance imaging, and amyloid positron emission tomography (PET) scans. Results We observed worse cognitive functioning in PTSD and TBI + PTSD groups, worse global cognitive functioning in the PTSD group, lower superior parietal volume in the TBI + PTSD group, and lower amyloid positivity in the PTSD group, but not the TBI group compared to controls without TBI/PTSD. Medial temporal lobe atrophy was not increased in the PTSD and/or TBI groups. Discussion Preliminary results do not indicate that TBI or PTSD increase the risk for AD measured by amyloid PET. Additional recruitment, longitudinal follow-up, and tau-PET scans will provide more information in the future