Alzheimer's disease (AD) research has entered a new era with the recent positive phase 3 clinical trials of the anti-Aβ antibodies lecanemab and donanemab. Why did it take 30 years to achieve these successes? Developing potent therapies for reducing fibrillar amyloid was key, as was selection of patients at relatively early stages of disease. Biomarkers of the target pathologies, including amyloid and tau PET, and insights from past trials were also critical to the recent successes. Moving forward, the challenge will be to develop more efficacious therapies with greater efficiency. Novel trial designs, including combination therapies and umbrella and basket protocols, will accelerate clinical development. Better diversity and inclusivity of trial participants are needed, and blood-based biomarkers may help to improve access for medically underserved groups. Incentivizing innovation in both academia and industry through public-private partnerships, collaborative mechanisms, and the creation of new career paths will be critical to build momentum in these exciting times.

Age-related changes in neural circuits, neural networks, and their plasticity are central to our understanding of age changes in cognition and brain structure and function. This paper summarizes selected findings on these topics presented at the Cognitive Aging Summit II. Specific areas discussed were synaptic vulnerability and plasticity, including the role of different types of synaptic spines, and hormonal effects in the dorsolateral prefrontal cortex of nonhuman primates, the impact of both compensatory processes and dedifferentiation on demand-dependent differences in prefrontal activation in relation to age and performance, the role of vascular disease, indexed by white matter signal abnormalities, on prefrontal activation during a functional magnetic resonance imaging-based cognitive control paradigm, and the influence of amyloid-β neuropathology on memory performance in older adults and the networks of brain activity underlying variability in performance. A greater understanding of age-related changes in brain plasticity and neural networks in healthy aging and in the presence of underlying vascular disease or amyloid pathology will be essential to identify new targets for intervention. Moreover, this understanding will assist in promoting the utilization of existing interventions, such as lifestyle and therapeutic modifiers of vascular disease.

Importance

Among cognitively normal individuals, elevated brain amyloid (defined by cerebrospinal fluid assays or positron emission tomography regional summaries) can be related to risk for later Alzheimer-related cognitive decline.

Objective

To characterize and quantify the risk for Alzheimer-related cognitive decline among cognitively normal individuals with elevated brain amyloid.

Design, setting, and participants

Exploratory analyses were conducted with longitudinal cognitive and biomarker data from 445 cognitively normal individuals in the United States and Canada. Participants were observed from August 23, 2005, to June 7, 2016, for a median of 3.1 years (interquartile range, 2.0-4.2 years; maximum follow-up, 10.3 years) as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI).

Exposures

Individuals were classified at baseline as having normal (n = 243) or elevated (n = 202) brain amyloid using positron emission tomography amyloid imaging or a cerebrospinal fluid assay of amyloid β.

Main outcomes and measures

Outcomes included scores on the Preclinical Alzheimer Cognitive Composite (PACC; a sum of 4 baseline standardized z scores, which decreases with worse performance), Mini-Mental State Examination (MMSE; 0 [worst] to 30 [best] points), Clinical Dementia Rating Sum of Boxes (CDR-Sum of Boxes; 0 [best] to 18 [worst] points), and Logical Memory Delayed Recall (0 [worst] to 25 [best] story units).

Results

Among the 445 participants (243 with normal amyloid, 202 with elevated amyloid), mean (SD) age was 74.0 (5.9) years, mean education was 16.4 (2.7) years, and 52% were women. The mean score for PACC at baseline was 0.00 (2.60); for MMSE, 29.0 (1.2); for CDR-Sum of Boxes, 0.04 (0.14); and for Logical Memory Delayed Recall, 13.1 (3.3). Compared with the group with normal amyloid, those with elevated amyloid had worse mean scores at 4 years on the PACC (mean difference, 1.51 points [95% CI, 0.94-2.10]; P < .001), MMSE (mean difference, 0.56 points [95% CI, 0.32-0.80]; P < .001), and CDR-Sum of Boxes (mean difference, 0.23 points [95% CI, 0.08-0.38]; P = .002). For Logical Memory Delayed Recall, between-group score was not statistically significant at 4 years (mean difference, 0.73 story units [95% CI, -0.02 to 1.48]; P = .056).

Conclusions and relevance

Exploratory analyses of a cognitively normal cohort followed up for a median of 3.1 years suggest that elevation in baseline brain amyloid level, compared with normal brain amyloid level, was associated with higher likelihood of cognitive decline, although the findings are of uncertain clinical significance. Further research is needed to assess the clinical importance of these differences and measure longer-term associations.

Importance

Alzheimer disease has a long preclinical stage characterized by β-amyloid (Aβ) accumulation without symptoms. Several trials focus on this stage and use biomarkers to include Aβ-positive participants, but an even earlier prevention of Aβ accumulation may be an effective treatment strategy.

Objective

To determine whether people who appear to be Aβ negative but are at high risk for Aβ positivity within the near future can be identified.

Design, setting, and participants

Longitudinal biomarker cohort study involving 35 cognitively healthy individuals who underwent cerebrospinal fluid (CSF) sampling for up to 3 years during the study (October 24, 2005, to September 1, 2014). All participants had normal CSF Aβ42 levels at baseline.

Main outcomes and measures

Predictors of future Aβ positivity (levels of CSF Aβ42 declining below a previously validated cutoff level of 192 ng/L) tested by random forest models. Tested predictors included levels of protein in the CSF, hippocampal volume, genetics, demographics, and cognitive scores.

Results

The CSF Aβ42 levels declined in 11 participants, and the CSF became Aβ positive. The baseline CSF Aβ42 level was a strong predictor of future positivity (accuracy, 79% [95% CI, 70%-87%]). Ten of 11 decliners had baseline CSF Aβ42 levels in the lower tertile of the reference range (<225 ng/L), and 22 of 24 nondecliners had baseline CSF Aβ42 levels in the upper 2 tertiles (≥225 ng/L). A high CSF P-tau level was associated with decline (accuracy, 68%; 95% CI, 55%-81%).

Conclusions and relevance

Baseline CSF Aβ42 levels in the lower part of the reference range are strongly associated with future Aβ positivity. This finding can be used in trials on very early prevention of Alzheimer disease to identify people at high risk for Aβ accumulation as defined by low CSF Aβ42 levels.

BACKGROUND AND OBJECTIVES: To develop and test the performance of the Positive Aβ Risk Score (PARS) for prediction of β-amyloid (Aβ) positivity in cognitively unimpaired individuals for use in clinical research. Detecting Aβ positivity is essential for identifying at-risk individuals who are candidates for early intervention with amyloid targeted treatments. METHODS: We used data from 4,134 cognitively normal individuals from the Anti-Amyloid Treatment in Asymptomatic Alzheimers (A4) Study. The sample was divided into training and test sets. A modified version of AutoScore, a machine learning-based software tool, was used to develop a scoring system using the training set. Three risk scores were developed using candidate predictors in various combinations from the following categories: demographics (age, sex, education, race, family history, body mass index, marital status, and ethnicity), subjective measures (Alzheimers Disease Cooperative Study Activities of Daily Living-Prevention Instrument, Geriatric Depression Scale, and Memory Complaint Questionnaire), objective measures (free recall, Mini-Mental State Examination, immediate recall, digit symbol substitution, and delayed logical memory scores), and APOE4 status. Performance of the risk scores was evaluated in the independent test set. RESULTS: PARS model 1 included age, body mass index (BMI), and family history and had an area under the curve (AUC) of 0.60 (95% CI 0.57-0.64). PARS model 2 included free recall in addition to the PARS model 1 variables and had an AUC of 0.61 (0.58-0.64). PARS model 3, which consisted of age, BMI, and APOE4 information, had an AUC of 0.73 (0.70-0.76). PARS model 3 showed the highest, but still moderate, performance metrics in comparison with other models with sensitivity of 72.0% (67.6%-76.4%), specificity of 62.1% (58.8%-65.4%), accuracy of 65.3% (62.7%-68.0%), and positive predictive value of 48.1% (44.1%-52.1%). DISCUSSION: PARS models are a set of simple and practical risk scores that may improve our ability to identify individuals more likely to be amyloid positive. The models can potentially be used to enrich trials and serve as a screening step in research settings. This approach can be followed by the use of additional variables for the development of improved risk scores. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that in cognitively unimpaired individuals PARS models predict Aβ positivity with moderate accuracy.

Introduction

The objective of this study was to develop a process to maximize the safety and effectiveness of disclosing Positron Emission Tomography (PET) amyloid imaging results to cognitively normal older adults participating in Alzheimer's disease secondary prevention studies such as the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) Study.

Methods

Using a modified Delphi Method to develop consensus on best practices, we gathered and analyzed data over three rounds from experts in two relevant fields: informed consent for genetic testing or human amyloid imaging.

Results

Experts reached consensus on (1) text for a brochure that describes amyloid imaging to a person who is considering whether to undergo such imaging in the context of a clinical trial, and (2) a process for amyloid PET result disclosure within such trials. Recommendations included: During consent, potential participants should complete an educational session, where they receive verbal and written information covering what is known and unknown about amyloid imaging, including possible results and their meaning, implications of results for risk of future cognitive decline, and information about Alzheimer's and risk factors. Participants should be screened for anxiety and depression to determine suitability to receive amyloid imaging information. The person conducting the sessions should check comprehension and be skilled in communication and recognizing distress. Imaging should occur on a separate day from consent, and disclosure on a separate day from imaging. Disclosure should occur in person, with time for questions. At disclosure, investigators should assess mood and willingness to receive results, and provide a written results report. Telephone follow-up within a few days should assess the impact of disclosure, and periodic scheduled assessments of depression and anxiety, with additional monitoring and follow-up for participants showing distress, should be performed.

Conclusions

We developed a document for use with potential study participants to describe the process of amyloid imaging and the implications of amyloid imaging results; and a disclosure process with attention to ongoing monitoring of both mood and safety to receive this information. This document and process will be used in the A4 Study and can be adapted for other research settings.

INTRODUCTION: To assess the relationship between memory performance defined by the Stages of Objective Memory Impairment (SOMI) system and the Alzheimers disease (AD) ATN (amyloid beta [A], pathologic tau [T], and neurodegeneration [N]) biomarker system. METHODS: We used data from the Harvard Aging Brain Study cohort to estimate the level of ATN biomarkers: amyloid beta (C-Pittsburgh compound B-positron emission tomography [PET]), tau (F-18-flortaucipir [FTP] PET), and neurodegeneration (magnetic resonance imaging volumetrics). We assessed the cross-sectional relationship of SOMI classification with global amyloid levels, entorhinal and inferior temporal tau deposition, and hippocampal atrophy. RESULTS: Participants with both memory storage and retrieval deficits (SOMI-3, -4) had smaller hippocampal volumes and higher entorhinal and inferior temporal tau burden than participants with no memory impairment (SOMI-0) or mild retrieval difficulty (SOMI-1). Amyloid burden did not differ among SOMI stages. DISCUSSION: This pilot supports the close relationship between tau pathology and memory impairment across the AD continuum.  SOMI may be useful to determine eligibility for randomized controlled trials prior to the assessment of biomarker status.

Objective

To elucidate the relationship between the two hallmark proteins of Alzheimer's disease (AD), amyloid-β (Aβ) and tau, and clinical decline over time among cognitively normal older individuals.

Design

A longitudinal cohort of clinically and cognitively normal older individuals assessed with baseline lumbar puncture and longitudinal clinical assessments.

Setting

Research centers across the United States and Canada.

Patients

We examined one hundred seven participants with a Clinical Dementia Rating (CDR) of 0 at baseline examination.

Main Outcome Measures

Using linear mixed effects models, we investigated the relationship between CSF p-tau_181p, CSF Aβ_1-42 and clinical decline as assessed using longitudinal change in global CDR, CDR-Sum of Boxes (CDR-SB), and the Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-cog).

Results

We found a significant relationship between decreased CSF Aβ_1-42 and longitudinal change in global CDR, CDR-SB, and ADAS-cog in individuals with elevated CSF p-tau_181p. In the absence of CSF p-tau_181p, the effect of CSF Aβ_1-42 on longitudinal clinical decline was not significantly different from zero.

Conclusions

In cognitively normal older individuals, Aβ-associated clinical decline over a mean of three years may occur only in the presence of ongoing, “downstream” neurodegeneration.

Blood-based markers (BBMs) have recently shown promise to revolutionize the diagnostic and prognostic work-up of Alzheimer's disease (AD), as well as to improve the design of interventional trials. Here we discuss in detail further research needed to be performed before widespread use of BBMs. We already now recommend use of BBMs as (pre-)screeners to identify individuals likely to have AD pathological changes for inclusion in trials evaluating disease-modifying therapies, provided the AD status is confirmed with positron emission tomography (PET) or cerebrospinal fluid (CSF) testing. We also encourage studying longitudinal BBM changes in ongoing as well as future interventional trials. However, BBMs should not yet be used as primary endpoints in pivotal trials. Further, we recommend to cautiously start using BBMs in specialized memory clinics as part of the diagnostic work-up of patients with cognitive symptoms and the results should be confirmed whenever possible with CSF or PET. Additional data are needed before use of BBMs as stand-alone diagnostic AD markers, or before considering use in primary care.

Objective

To examine associations between aggregate genetic risk and Alzheimer disease (AD) markers in stages preceding the clinical symptoms of dementia using data from 2 large observational cohort studies.

Methods

We computed polygenic risk scores (PGRS) using summary statistics from the International Genomics of Alzheimer's Project genome-wide association study of AD. Associations between PGRS and AD markers (cognitive decline, clinical progression, hippocampus volume, and β-amyloid) were assessed within older participants with dementia. Associations between PGRS and hippocampus volume were additionally examined within healthy younger participants (age 18-35 years).

Results

Within participants without dementia, elevated PGRS was associated with worse memory (p = 0.002) and smaller hippocampus (p = 0.002) at baseline, as well as greater longitudinal cognitive decline (memory: p = 0.0005, executive function: p = 0.01) and clinical progression (p < 0.00001). High PGRS was associated with AD-like levels of β-amyloid burden as measured with florbetapir PET (p = 0.03) but did not reach statistical significance for CSF β-amyloid (p = 0.11). Within the younger group, higher PGRS was associated with smaller hippocampus volume (p = 0.05). This pattern was evident when examining a PGRS that included many loci below the genome-wide association study (GWAS)-level significance threshold (16,123 single nucleotide polymorphisms), but not when PGRS was restricted to GWAS-level significant loci (18 single nucleotide polymorphisms).

Conclusions

Effects related to common genetic risk loci distributed throughout the genome are detectable among individuals without dementia. The influence of this genetic risk may begin in early life and make an individual more susceptible to cognitive impairment in late life. Future refinement of polygenic risk scores may help identify individuals at risk for AD dementia.