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The National Institute on Aging and the Alzheimer's Association convened three separate work groups in 2011 and single work groups in 2012 and 2018 to create recommendations for the diagnosis and characterization of Alzheimer's disease (AD). The present document updates the 2018 research framework in response to several recent developments. Defining diseases biologically, rather than based on syndromic presentation, has long been standard in many areas of medicine (e.g., oncology), and is becoming a unifying concept common to all neurodegenerative diseases, not just AD. The present document is consistent with this principle. Our intent is to present objective criteria for diagnosis and staging AD, incorporating recent advances in biomarkers, to serve as a bridge between research and clinical care. These criteria are not intended to provide step‐by‐step clinical practice guidelines for clinical workflow or specific treatment protocols, but rather serve as general principles to inform diagnosis and staging of AD that reflect current science. Highlights We define Alzheimer's disease (AD) to be a biological process that begins with the appearance of AD neuropathologic change (ADNPC) while people are asymptomatic. Progression of the neuropathologic burden leads to the later appearance and progression of clinical symptoms. Early‐changing Core 1 biomarkers (amyloid positron emission tomography [PET], approved cerebrospinal fluid biomarkers, and accurate plasma biomarkers [especially phosphorylated tau 217]) map onto either the amyloid beta or AD tauopathy pathway; however, these reflect the presence of ADNPC more generally (i.e., both neuritic plaques and tangles). An abnormal Core 1 biomarker result is sufficient to establish a diagnosis of AD and to inform clinical decision making throughout the disease continuum. Later‐changing Core 2 biomarkers (biofluid and tau PET) can provide prognostic information, and when abnormal, will increase confidence that AD is contributing to symptoms. An integrated biological and clinical staging scheme is described that accommodates the fact that common copathologies, cognitive reserve, and resistance may modify relationships between clinical and biological AD stages.

Background Lecanemab, a humanized IgG1 monoclonal antibody that targets soluble aggregated Aβ species (protofibrils), has demonstrated robust brain fibrillar amyloid reduction and slowing of clinical decline in early AD. The objective of this analysis is to report results from study 201 blinded period (core), the open-label extension (OLE), and gap period (between core and OLE) supporting the effectiveness of lecanemab. Methods The lecanemab study 201 core was a double-blind, randomized, placebo-controlled study of 856 patients randomized to one of five dose regimens or placebo. An OLE of study 201 was initiated to allow patients to receive open-label lecanemab 10mg/kg biweekly for up to 24 months, with an intervening off-treatment period (gap period) ranging from 9 to 59 months (mean 24 months). Results At 12 and 18 months of treatment in the core, lecanemab 10 mg/kg biweekly demonstrated dose-dependent reductions of brain amyloid measured PET and corresponding changes in plasma biomarkers and slowing of cognitive decline. The rates of clinical progression during the gap were similar in lecanemab and placebo subjects, with clinical treatment differences maintained after discontinued dosing over an average of 24 months in the gap period. During the gap, plasma Aβ42/40 ratio and p-tau181 levels began to return towards pre-randomization levels more quickly than amyloid PET. At OLE baseline, treatment differences vs placebo at 18 months in the randomized period were maintained across 3 clinical assessments. In the OLE, lecanemab 10 mg/kg biweekly treatment produced dose-dependent reductions in amyloid PET SUVr, improvements in plasma Aβ42/40 ratio, and reductions in plasma p-tau181. Conclusions Lecanemab treatment resulted in significant reduction in amyloid plaques and a slowing of clinical decline. Data indicate that rapid and pronounced amyloid reduction correlates with clinical benefit and potential disease-modifying effects, as well as the potential to use plasma biomarkers to monitor for lecanemab treatment effects. Trial registration ClinicalTrials.gov NCT01767311 .

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.To date, all phase III trials of β-site amyloid precursor protein (APP)-cleaving enzyme (BACE) inhibitors for Alzheimer disease were either discontinued or produced negative results. Here the authors present their opinion that BACE inhibitors still hold promise as a preventative therapy for Alzheimer disease and outline a series of experiments to inform future trials.

The biomarker model of Alzheimer's disease postulates a dynamic sequence of amyloidosis, neurodegeneration, and cognitive decline as an individual progresses from preclinical Alzheimer's disease to dementia. Despite supportive evidence from cross-sectional studies, verification with long-term within-individual data is needed. For this prospective cohort study, carriers of autosomal dominant Alzheimer's disease mutations (aged ≥21 years) were recruited from across the USA through referrals by physicians or from affected families. People with mutations in PSEN1, PSEN2, or APP were assessed at the University of Pittsburgh Alzheimer's Disease Research Center every 1–2 years, between March 23, 2003, and Aug 1, 2014. We measured global cerebral amyloid β (Aβ) load using 11C-Pittsburgh Compound-B PET, posterior cortical metabolism with 18F-fluorodeoxyglucose PET, hippocampal volume (age and sex corrected) with T1-weighted MRI, verbal memory with the ten-item Consortium to Establish a Registry for Alzheimer's Disease Word List Learning Delayed Recall Test, and general cognition with the Mini Mental State Examination. We estimated overall biomarker trajectories across estimated years from symptom onset using linear mixed models, and compared these estimates with cross-sectional data from cognitively normal control individuals (age 65–89 years) who were negative for amyloidosis, hypometabolism, and hippocampal atrophy. In the mutation carriers who had the longest follow-up, we examined the within-individual progression of amyloidosis, metabolism, hippocampal volume, and cognition to identify progressive within-individual changes (a significant change was defined as an increase or decrease of more than two Z scores standardised to controls). 16 people with mutations in PSEN1, PSEN2, or APP, aged 28–56 years, completed between two and eight assessments (a total of 83 assessments) over 2–11 years. Significant differences in mutation carriers compared with controls (p<0·01) were detected in the following order: increased amyloidosis (7·5 years before expected onset), decreased metabolism (at time of expected onset), decreased hippocampal volume and verbal memory (7·5 years after expected onset), and decreased general cognition (10 years after expected onset). Among the seven participants with longest follow-up (seven or eight assessments spanning 6–11 years), three individuals had active amyloidosis without progressive neurodegeneration or cognitive decline, two amyloid-positive individuals showed progressive neurodegeneration and cognitive decline without further progressive amyloidosis, and two amyloid-positive individuals showed neither active amyloidosis nor progressive neurodegeneration or cognitive decline. Our results support amyloidosis as the earliest component of the biomarker model in autosomal dominant Alzheimer's disease. Our within-individual examination suggests three sequential phases in the development of autosomal dominant Alzheimer's disease—active amyloidosis, a stable amyloid-positive period, and progressive neurodegeneration and cognitive decline—indicating that Aβ accumulation is largely complete before progressive neurodegeneration and cognitive decline occur. These findings offer supportive evidence for efforts to target early Aβ deposition for secondary prevention in individuals with autosomal dominant Alzheimer's disease. National Institutes of Health and Howard Hughes Medical Institute.

In this study of a cohort of adults with genetic mutations that cause autosomal dominant Alzheimer's disease, researchers identified abnormalities in cerebrospinal fluid biomarkers and neuroimaging tests that develop decades before the onset of dementia. Alzheimer's disease is the most common cause of dementia and is currently estimated to affect more than 5 million people in the United States, with an expected increase to 13 million by the year 2050. The typical clinical presentation is progressive loss of memory and cognitive function, ultimately leading to a loss of independence and causing a heavy personal toll on the patient and the family. The costs of care of patients with Alzheimer's disease in 2010 were estimated at more than $172 billion in the United States, an annual cost that is predicted to increase to a trillion dollars . . .

Alzheimer disease (AD) prevention trials hold the promise to delay or prevent cognitive decline and dementia onset by intervening before significant neuronal damage occurs. In recent years, the first AD prevention trials have launched and are yielding important findings on the biology of targeting asymptomatic AD pathology. However, there are limitations that impact the design of these prevention trials, including the translation of animal models that recapitulate key stages and multiple pathological aspects of the human disease, missing target validation in asymptomatic disease, uncertain causality of the association of pathophysiologic changes with cognitive and clinical symptoms, and limited biomarker validation for novel targets. The field is accelerating advancements in key areas including the development of highly specific and quantitative biomarker measures for AD pathology, increasing our understanding of the course and relationship of amyloid and tau pathology in asymptomatic through symptomatic stages, and the development of powerful interventions that can slow or reverse AD amyloid pathology. We review the current status of prevention trials and propose key areas of needed research as a call to basic and translational scientists to accelerate AD prevention. Specifically, we review (1) sporadic and dominantly inherited primary and secondary AD prevention trials, (2) proposed targets, mechanisms, and drugs including the amyloid, tau, and inflammatory pathways and combination treatments, (3) the need for more appropriate prevention animal models and experiments, and (4) biomarkers and outcome measures needed to design human asymptomatic prevention trials. We conclude with actions needed to effectively move prevention targets and trials forward.

Genetic studies of Alzheimer disease (AD) have prioritized variants in genes related to the amyloid cascade, lipid metabolism, and neuroimmune modulation. However, the cell-specific effect of variants in these genes is not fully understood. Here, we perform single-nucleus RNA-sequencing (snRNA-seq) on nearly 300,000 nuclei from the parietal cortex of AD autosomal dominant ( APP and PSEN1 ) and risk-modifying variant ( APOE, TREM2 and MS4A ) carriers. Within individual cell types, we capture genes commonly dysregulated across variant groups. However, specific transcriptional states are more prevalent within variant carriers. TREM2 oligodendrocytes show a dysregulated autophagy-lysosomal pathway, MS4A microglia have dysregulated complement cascade genes, and APOE ε4 inhibitory neurons display signs of ferroptosis. All cell types have enriched states in autosomal dominant carriers. We leverage differential expression and single-nucleus ATAC-seq to map GWAS signals to effector cell types including the NCK2 signal to neurons in addition to the initially proposed microglia. Overall, our results provide insights into the transcriptional diversity resulting from AD genetic architecture and cellular heterogeneity. The data can be explored on the online browser ( http://web.hararilab.org/SNARE/ ). Mutations in amyloid precursor protein (APP) and presenilin 1 (PSEN1) cause autosomal dominant AD (ADAD). Here, the authors perform single-nucleus RNA-sequencing of ADAD and other disease risk modifying variant carriers and report altered expression states of specific brain cell types.

•A machine learning model can predict age from functional connectivity in controls.•Model estimates of age are significantly elevated in symptomatic Alzheimer disease.•Model estimates of age are surprisingly reduced in preclinical Alzheimer disease.•Distinct functional networks were sensitive to age and Alzheimer differences. “Brain-predicted age” quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18–89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker.

Although amyloid b immunotherapies offer great potential for prevention or delay of symptoms in dominantly inherited AD (DIAD), the mechanism of action of this class of medications does not address the underlying mechanism of most DIAD mutations. Moreover, the need for repeated IV infusions or sub‐cutaneous injections with Ab immunotherapies may prove challenging for long‐term prevention approaches. The majority of DIAD mutations appear to affect the interaction of the gamma‐secretase enzyme with the Amyloid Precursor Protein (APP) making this enzyme an attractive target for disease modification. However, emerging evidence also suggests that different DIAD mutations can have significantly different effects on the processing of APP, which suggests that targeting this enzyme may have mutation dependent effects as well. Developments in the understanding of the timing of amyloid b‐dependent changes in DIAD and the development of accurate measurements of Ab now enable precision‐based approaches necessary for targeting gamma‐secretase. Moreover, the presence of clinical trial platforms for DIAD facilitate the implementation of trials for targeting gamma‐secretase. In this session, we will review approaches to testing next generation gamma‐secretase therapies in DIAD. Specifically, we will outline prevention approaches in DIAD that leverage fluid‐based biomarker measurements of Ab ‐peptides and sensitive, brief cognitive assessments to determine optimal mutation specific dosing approaches‐ i.e. precision medicine approach. Combining plasma, CSF and molecular PET imaging, we will outline how to assess gamma‐secretase targeting therapies for different mutations in DIAD along a timescale that utilizes early pharmacodynamic effects on Ab ‐peptides to predict long‐term effects on Ab ‐and downstream biomarkers of AD.