Alzheimer's Disease Clinical Trials Research Articles

Research Attitudes Questionnaire scores and retention in a recruitment registry

Background Recruitment registries are maximally effective when registrants are retained to the point of referral. The Research Attitudes Questionnaire (RAQ) has previously been shown to predict research participation behaviors, including Alzheimer's disease clinical trial completion. Objective To test the hypothesis that RAQ score is associated with retention behaviors in a local recruitment registry. Methods Using data from the UC Irvine Consent-to-Contact Registry, a recruitment registry that enrolls adults 18 years and older, we used logistic regression to quantify the association of RAQ score and the odds of first-year non-renewal. Covariates included demographic variables, comorbidities, and recruitment source. In longitudinal analyses, we used discrete proportional hazards and Cox proportional hazards models to quantify the relationship between RAQ score and time to non-renewal and time to active withdrawal, respectively. Results Among n = 4663 participants, we estimated that a 5-point higher baseline RAQ score was associated with a 15% lower odds of first-year non-renewal, after adjustment for potential confounding factors (OR: 0.85, 95% CI: (0.79, 0.92), p < 0.001). Older age and higher education were also associated with lower odds of non-renewal while Asian race, Hispanic ethnicity, and certain recruitment sources (e.g., doctor or friend referral) were associated with higher odds of non-renewal. Higher baseline RAQ and higher annually updated RAQ were both significantly associated with lower odds of non-renewal longitudinally. Age, education, and some recruitment sources, but not RAQ, were associated with active withdrawal. Conclusions Opportunities exist to identify predictors of registry retention behaviors and possible targets for intervention to improve related outcomes.

The Missing Link in Antiamyloid Therapy.

Alzheimer's disease (AD) impacts millions of elderly adults worldwide causing cognitive decline and severe deterioration of activities of daily life. The popular causal hypotheses for several decades include beta-amyloid (Aβ) deposition and tau hyperphosphorylation. AD research and more than 34% of clinical trials in AD are based on these two hypotheses. A phase-III clinical trial of lecanemab in early AD and mild cognitive impaired (MCI) patients reported a delay in cognitive decline of 27% over an 18-month treatment schedule. This multicenter trial found high specificity of lecanemab toward toxic protofibrils and subsequent clearance of beta-amyloid. There were, however, adverse events, which included cerebral edema and intracerebral hemorrhages in 23.1% of patients compared to 9.3% for those who received a placebo. Suboptimal clinical outcomes, brain volume loss, and adverse events in lecanemab treatment prompted a search for an alternative etiopathogenic explanation. Our research and others have focused on the oxidative stress (OS) hypothesis in AD. Autopsy studies have found significant depletion of the master antioxidant glutathione (GSH) in the hippocampal region, and is believed to be an early event in AD progression. Hippocampal GSH depletion is positively correlated with memory impairment. We have confirmed non-invasively with magnetic resonance spectroscopy (MRS) the depletion of GSH in patients with MCI and AD. We therefore propose a combinational therapy involving oral supplementation of gamma-glutamylcysteine (GGC), an early precursor of glutathione, to replenish brain GSH in addition to lecanemab, potentially to maximize desirable outcomes from combined therapeutic approach.

Artificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials.

Investigators conducting clinical trials have an ethical, scientific, and regulatory obligation to protect the safety of trial participants. Traditionally, safety monitoring includes manual review and coding of adverse event data by expert clinicians. Our study explores the use of natural language processing (NLP) and artificial intelligence (AI) methods to streamline and standardize clinician coding of adverse event data in Alzheimer's disease (AD) clinical trials. Our quantitative retrospective study aimed to develop a gold standard AD adverse event data set, evaluate the predictive performance of NLP-based models to classify adverse events, and determine whether automated coding is more efficient, accurate, reliable, and consistent than clinician coding. Our study was conducted at the University of Southern California's Alzheimer's Therapeutic Research Institute (ATRI). ATRI serves as the clinical and data coordinating center for the Alzheimer's Clinical Trial Consortium (ACTC). We collected demographic and adverse event data from eight completed clinical trials in participants (n=1920) with symptomatic AD conducted between 2005 and 2020. Original expert clinician-confirmed codes were used for all model performance comparisons. F1 score was used as the primary model selection metric. Final classifier performance was evaluated using predictive accuracy. Clinician effort was measured in time to code, review, and confirm coded adverse events. In a sample of 1000 adverse events, AI-based AE coding achieved higher accuracy (∼20% increase in accuracy) and was more cost-effective (∼80% cost reduction) than traditional clinician coding. Our study results demonstrate how approaches that effectively combine AI and human expertise can improve the efficiency and quality of adverse event coding and clinical trial safety monitoring.

Blood phosphorylated Tau181 reliably differentiates amyloid‐positive from amyloid‐negative subjects in the Alzheimer's disease continuum: A systematic review and meta‐analysis

AbstractINTRODUCTIONBlood‐based biomarkers seem promising for the diagnosis of Alzheimer's disease (AD).METHODSWe performed a systematic review and meta‐analysis on the potential of blood phosphorylated Tau181 (p‐tau181) to differentiate amyloid‐positive (A+) and amyloid‐negative (A−) subjects. Two meta‐analyses were conducted, showing the mean p‐tau values in blood and cerebrospinal fluid (CSF) in the A+ and A− group, and the second comparing the mean p‐tau concentrations in blood and CSF among A+ versus A‐ participants, by laboratory assessment method.RESULTSEighteen studies (2764 A+ and 5646 A− subjects) were included. The single‐group meta‐analysis showed mean higher blood p‐tau181 values in the A+ than in the A− group. In the head‐to‐head meta‐analysis, blood p‐tau reliably differentiated A+ patients from A− participants.DISCUSSIONRegardless of the laboratory technique, blood p‐tau181 reliably differentiates A+ and A− subjects. Therefore, it might have important applications for early diagnosis and inclusion in clinical trials for AD patients.Highlights The role of blood‐based biomarkers in discriminating AD patients is still uncertain. Blood p‐tau181 distinguishes among amyloid‐positive and amyloid‐negative subjects. Blood p‐tau181 might allow early diagnosis and inclusion in clinical trials.

Key considerations for combination therapy in Alzheimer's clinical trials: Perspectives from an expert advisory board convened by the Alzheimer's drug discovery foundation.

There is growing consensus in the Alzheimer's community that combination therapy will be needed to maximize therapeutic benefits through the course of the disease. However, combination therapy raises complex questions and decisions for study sponsors, from preclinical research through clinical trial design to regulatory, statistical, and operational considerations. In January 2024, the Alzheimer's Drug Discovery Foundation convened an expert advisory board to discuss the key considerations in each of these areas. Experts agreed on the need to prioritize a combination therapy approach that encompasses a wide range of targets associated with aging and the underlying biology of Alzheimer's disease. Progress in combination therapy could be accelerated by leveraging preclinical research and Phase 1 and 2A trials to identify the most promising combinations for further development, exploring repurposed agents with available preclinical and clinical data, building collaborations across sectors to support operational challenges, and planning for the likely impact of anti-amyloid beta-protein monoclonal antibody therapies on future clinical trial designs.

Temporal trends in mental health terminology in Alzheimer's disease clinical trials.

Despite several studies having correlated Alzheimer's disease with mental health conditions, the extent to which they have been incorporated into Alzheimer's disease clinical trials remains unclear. This study aimed to assess the temporal trends in mental health-related terminology in Alzheimer's disease clinical trials as a proxy measure of research interest. Additionally, it sought to determine the effect of the COVID-19 pandemic on the frequency of these terms through pre-pandemic and post-pandemic trend assessment. In this retrospective descriptive analysis, we included 2243 trials with a start date between 1988 and 2022 by searching for the keyword "Alzheimer Disease" in the U.S. National Library of Medicine ClinicaTrials.gov database. A Python program was created to extract and count the frequency of four mental health terms (loneliness, depression, anxiety, and distress) by year and trial status (e.g., completed, active, recruiting). Binary logistic regression analyses were conducted to examine the yearly patterns in the appearance of the four mental health terms. A multivariable logistic regression analysis was performed to identify trial characteristics associated with each mental health term. Our results depicted a statistically significant increasing trend in three (i.e., loneliness, anxiety, distress) of the four mental health conditions by year. A comparison between pre-pandemic and post-pandemic trials showed an increase in the mention of the same three words over time. These results may suggest a growing awareness of mental health conditions and a greater interest in considering these conditions in Alzheimer's disease trials, particularly after the onset of COVID-19. Future researchers should conduct more in-depth analyses to examine how mental health variables are operationalized in these trials, with consideration for their subsequent success.

Impact of gender on the willingness to participate in clinical trials and undergo related procedures in individuals from an Alzheimer’s prevention research cohort

BackgroundAlthough there is growing evidence of the association between gender and early diagnosis of preclinical Alzheimer's disease, little attention has been given to the enrolment ratio of men and women in clinical trials and data reporting.MethodsThis study aims to analyze gender differences in sociodemographic factors associated with the willingness to participate in clinical trials and undergo specific procedures in the context of an Alzheimer's disease prevention research cohort. 2544 cognitively unimpaired participants from the ALFA parent cohort (age 45–75 years) of the Barcelonaβeta Brain Research Center were contacted through a structured phone call to determine their willingness to participate in Alzheimer's disease clinical trials and undergo trial-related procedures (magnetic resonance imaging, lumbar puncture, positron emission tomography, and cognitive assessment). Sociodemographic data on education, occupational attainment, civil and caregiver status were gathered. Stepwise logistic regression models were performed in order to study the interaction between gender and sociodemographic factors in the willingness to participate in clinical trials and to undergo clinical trial-related procedures.Results1,606 out of the 2,544 participants were women (63.1%). Women were significantly younger and had lower educational attainment compared with men. In addition, women were more likely to be caregivers, single and unemployed. Women showed a significantly lower willingness than men to participate in a clinical trial (p = 0.003) and to undergo a lumbar puncture (p < 0.001). Single women were less willing to participate in clinical trials than single men (p = 0.041). Regarding clinical trial-related procedures, women with higher years of education were significantly less willing to undergo a lumbar puncture (p = 0.031).ConclusionWe found gender differences regarding the sociodemographic factors that predict the willingness to participate in clinical trials and to undergo clinical trial-related procedures. Our results highlight the urgent need to design recruitment strategies accounting for gender-related factors, particularly those related to marital status and education.

Mixture Disease Progression Model to Predict and Cluster the Long-Term Trajectory of Cognitive Decline in Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disease for which many clinical trials failed to detect treatment effects, possibly due to the heterogeneity of disease progression among the patients. Predicting and clustering a long-term trajectory of cognitive decline from the short-term cognition data of individual patients would help develop therapeutic interventions for AD. This study developed mixture disease progression model to predict and cluster the long-term trajectory of cognitive decline in the population. We predicted the 30-year long-term trajectories of the three cognitive scales and categorized the individuals into rapid and slow cognitive decliners by applying the method, which was based on the two-component normal mixture nonlinear mixed-effects model, to the short-term follow-up data of the Mini-Mental State Examination, the 13-item Alzheimer's Disease Assessment Scale-Cognitive, and the Clinical Dementia Rating Scale-sum of boxes collected in patients with mild cognitive impairment and AD in the Alzheimer's Disease Neuroimaging Initiative. For each cognitive scale, the models identified two distinct subpopulations, including a population of comprising approximately 10-20% of individuals experiencing rapid cognitive decline, wherein the posterior means of the differences in cognitive decline speed between the two groups ranged from 2 to 3 years. We also identified baseline background factors associated with rapid decliners for three cognitive scales. Identifying the risk factors associated with rapid decline of cognition by the proposed method aids in planning eligibility criteria and allocation strategy for accounting for the varying disease progression speeds among the patients enrolled in clinical trials for AD.

Timeline to symptomatic Alzheimer's disease in people with Down syndrome as assessed by amyloid-PET and tau-PET: a longitudinal cohort study

Adults with Down syndrome are at risk for Alzheimer's disease. Natural history cohort studies have characterised the progression of Alzheimer's disease biomarkers in people with Down syndrome, with a focus on amyloid β-PET and tau-PET. In this study, we aimed to leverage these well characterised imaging biomarkers in a large cohort of individuals with Down syndrome, to examine the timeline to symptomatic Alzheimer's disease based on estimated years since the detection on PET of amyloid β-positivity, referred to here as amyloid age, and in relation to tau burden as assessed by PET. In this prospective, longitudinal, observational cohort study, data were collected at four university research sites in the UK and USA as part of the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS) study. Eligible participants were aged 25 years or older with Down syndrome, had a mental age of at least 3 years (based on a standardised intelligence quotient test), and had trisomy 21 (full, mosaic, or translocation) confirmed through karyotyping. Participants were assessed twice between 2017 and 2022, with approximately 32 months between visits. Participants had amyloid-PET and tau-PET scans, and underwent cognitive assessment with the modified Cued Recall Test (mCRT) and the Down Syndrome Mental Status Examination (DSMSE) to assess cognitive functioning. Study partners completed the National Task Group-Early Detection Screen for Dementia (NTG-EDSD). Generalised linear models were used to assess the association between amyloid age (whereby 0 years equated to 18 centiloids) and mCRT, DSMSE, NTG-EDSD, and tau PET at baseline and the 32-month follow-up. Broken stick regression was used to identify the amyloid age that corresponded to decreases in cognitive performance and increases in tau PET after the onset of amyloid β positivity. 167 adults with Down syndrome, of whom 92 had longitudinal data, were included in our analyses. Generalised linear regressions showed significant quadratic associations between amyloid age and cognitive performance and cubic associations between amyloid age and tau, both at baseline and at the 32-month follow-up. Using broken stick regression models, differences in mCRT total scores were detected beginning 2·7 years (95% credible interval [CrI] 0·2 to 5·4; equating to 29·8 centiloids) after the onset of amyloid β positivity in cross-sectional models. Based on cross-sectional data, increases in tau deposition started a mean of 2·7-6·1 years (equating to 29·8-47·9 centiloids) after the onset of amyloid β positivity. Mild cognitive impairment was observed at a mean amyloid age of 7·4 years (SD 6·6; equating to 56·8 centiloids) and dementia was observed at a mean amyloid age of 12·7 years (5·6; equating to 97·4 centiloids). There is a short timeline to initial cognitive decline and dementia from onset of amyloid β positivity and tau deposition in people with Down syndrome. This newly established timeline based on amyloid age (or equivalent centiloid values) is important for clinical practice and informing the design of Alzheimer's disease clinical trials, and it avoids the limitations of timelines based on chronological age. National Institute on Aging and the National Institute for Child Health and Human Development.

Exploring the utility of Plasma GFAP as a secondary endpoint in Alzheimer's disease Clinical trials to monitor disease progression

Exploring the utility of Plasma GFAP as a secondary endpoint in Alzheimer's disease Clinical trials to monitor disease progression

(In)Equity in Alzheimer’s Clinical Trials: A Long Road Ahead?

AbstractBackgroundDementia is a complex condition that affects individuals in various ways around the world, with a particularly high prevalence and incidence in low‐ to middle‐income countries. However, the majority of clinical trials on immunomodulators in Alzheimer’s disease have predominantly been conducted in specific geographic regions and populations. Our objective is to assess the diversity and inclusiveness of the participant samples in terminated phase 2 or 3 clinical trials for anti‐amyloid therapies.MethodWe performed a systematic review for completed and terminated phase 2 or 3 clinical trials for Alzheimer’s disease in ClinicalTrials.gov with results published. We also searched for MEDLINE using “((anti‐amyloid) AND (alzheimer disease[MeSH Terms])) AND (therapy[MeSH Terms] OR (drug) OR (trial))”. We selected only trials using anti‐amyloid therapies regardless of the disease stage (Figure 1. Flowchart). We included all studies that provided sociodemographic characteristics of the sample studied. Variables collected were gender, race, countries where the trial was conducted, and mean years of education.ResultA total of 16055 individuals over 12 trials were evaluated in this work (ENGAGE, EMERGE, GRADUATE I and II, CLARITY‐AD, A4, EXPEDITION3, ELN115727, CREAD and CREAD 2, TRAILBLAZER‐ALZ 1 and 2). Gender was similarly distributed among studies (8911 females, 55.5%). Participants included were mostly white individuals (13664, 85.1%), with a minority of asian individuals (1259, 7.8%), and black individuals (202, 1.2%). Ethnic origins were demonstrated in 8 studies and only 774 (4.8%) individuals self‐reported as being hispanic. Studies were conducted in 164 countries taken altogether, but included only 25 (15.2%) low‐ and middle‐income countries.ConclusionClinical trials using anti‐amyloid drugs have failed to represent the majority of individuals living with AD‐related cognitive impairment on a global scale. It ultimately increases the disparities presented in the diagnosis and treatment of individuals with dementia. Ideally, anti‐amyloid trials should incorporate the epidemiology of individuals with dementia worldwide. These observations underscore the urgent need for coordinated efforts involving scientists, societies, local governments, and industry to prioritize equity as a central goal in science.

Accelerating randomized clinical trials in Alzheimer’s Disease using generative machine learning model forecasts of progression

Accelerating randomized clinical trials in Alzheimer’s Disease using generative machine learning model forecasts of progression

Inclusive Clinical Trials in Alzheimer’s Disease: A Latin American Perspective from LatAm FINGERS

Inclusive Clinical Trials in Alzheimer’s Disease: A Latin American Perspective from LatAm FINGERS

Distinct medical and substance use histories associate with cognitive decline in Alzheimer's Disease.

Phenotype clustering reduces patient heterogeneity and could be useful when designing precision clinical trials. We hypothesized that the onset of early cognitive decline in patients would exhibit variance predicated on the clinical history documented prior to an Alzheimer's Disease (AD) diagnosis. Self-reported medical and substance use history (i.e., problem history) was used to cluster participants from the National Alzheimer's Coordinating Centers (NACC) into distinct subtypes. Linear mixed effects modeling was used to determine the effect of problem history subtype on cognitive decline over two years. 2754 individuals were partitioned into three subtypes: minimal (n = 1380), substance use (n = 1038), and cardiovascular (n = 336) subtypes. The cardiovascular problem history subtype had significantly worse cognitive decline over a two-year follow-up period (p = 0.013). Our study highlights the need to account for problem history to reduce heterogeneity of outcomes in AD clinical trials.

Unlocking Cognitive Analysis Potential in Alzheimer's Disease Clinical Trials: Investigating Hierarchical Linear Models for Analyzing Novel Measurement Burst Design Data.

Measurement burst designs typically administer brief cognitive tests four times per day for 1 week, resulting in a maximum of 28 data points per week per test for every 6 months. In Alzheimer's disease clinical trials, utilizing measurement burst designs holds great promise for boosting statistical power by collecting huge amount of data. However, appropriate methods for analyzing these complex datasets are not well investigated. Furthermore, the large amount of burst design data also poses tremendous challenges for traditional computational procedures such as SAS mixed or Nlmixed. We propose to analyze burst design data using novel hierarchical linear mixed effects models or hierarchical mixed models for repeated measures. Through simulations and real-world data applications using the novel SAS procedure Hpmixed, we demonstrate these hierarchical models' efficiency over traditional models. Our sample simulation and analysis code can serve as a catalyst to facilitate the methodology development for burst design data.

The ADNI Administrative Core: Ensuring ADNI's success and informing future AD clinical trials.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) Administrative Core oversees and coordinates all ADNI activities, to ensure the success and maximize the impact of ADNI in advancing Alzheimer's disease (AD) research and clinical trials. It manages finances and develops policies for data sharing, publications using ADNI data, and access to ADNI biospecimens. The Core develops and executes pilot projects to guide future ADNI activities and identifies key innovative methods for inclusion in ADNI. For ADNI4, the Administrative Core collaborates with the Engagement, Clinical, and Biomarker Cores to develop and evaluate novel, digital methods and infrastructure for participant recruitment, screening, and assessment of participants. The goal of these efforts is to enroll 500 participants, including>50% from underrepresented populations, 40% with mild cognitive impairment, and 80% with elevated AD biomarkers. This new approach also provides a unique opportunity to validate novel methods. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) Administrative Core oversees and coordinates all ADNI activities. The overall goal is to ensure ADNI's success and help design future Alzheimer's disease (AD) clinical trials. A key innovation is data sharing without embargo to maximize scientific impact. For ADNI4, novel, digital methods for recruitment and assessment were developed. New methods are designed to improve the participation of underrepresented populations.

Current Bioactive Compounds in the Treatment of Alzheimer's Disease

Abstract: Worldwide, more than 44 million individuals are living with Alzheimer's disease (AD), making it the most common type of dementia. Because neuroinflammation is so important in the development of AD, anti-inflammatory tactics may be a promising avenue for treatment. Searches were conducted in Scopus, the Web of Science, and PubMed using the following keywords: phytoconstituents, AD, traditional medicine, and Chinese herbs. Therefore, the purpose of this review was to summarise the known phytochemistry and current state of the chosen plant species. However, there has been limited effectiveness in clinical trials for AD with currently available anti-inflammatory medicines. This study brings together the latest findings in the treatment of AD using natural substances, specifically phytochemicals, which have anti-inflammatory, antioxidant, and neuroprotective characteristics. Although there has been little success with existing anti-inflammatory medications, there is hope for targeting molecular pathways associated with AD, including Aβ overproduction, apoptosis, oxidative stress, and mitochondrial dysfunction, through the use of natural bioactive chemicals such alkaloids, polyphenols, and terpenes. The promise of natural compounds as safer alternatives or supplementary therapies to current treatments for Alzheimer's disease is highlighted in this study, which focuses on their ability to alleviate major pathogenic processes in the disease. Their medicinal potential and effectiveness can be enhanced with additional research.

Assessing clinical progression measures in Alzheimer's disease trials: A systematic review and meta-analysis.

Assessing treatments for Alzheimer's disease (AD) relies on reliable tools for measuring AD progression. In this analysis, we evaluate the sensitivity of clinical progression measures in AD within randomized controlled trials (RCTs) with confirmed positive amyloid (Aβ+) status prior to trial enrollment. Excluding trials targeting non-cognitive symptoms, we conducted meta-analyses on progression measures from 25 selected RCTs using R version 4.2.0, along with the metafor and emmeans libraries. The Functional Activities Questionnaire (FAQ) demonstrated the greatest sensitivity over 12weeks. Other cognitive measures demonstrated lower sensitivity. The integrated Alzheimer's Disease Rating Scale (iADRS) and Clinical Dementia Rating-Sum of Boxes (CDR-SB) seemed more effective than their individual cognitive components. Neuropsychiatric measures were the least sensitive in measuring progression. Functional measures generally outperformed other measure categories. Purely cognitive domain-based measures were suboptimal for tracking early AD progression. Ideally, future measures should incorporate both cognitive and functional components to enhance sensitivity. Concerns remain regarding the limitations of current outcome measures used in AD clinical trials, particularly their sensitivity in the early and preclinical stages of the disease, which hampers their reliability as indicators of AD progression. The Functional Activities Questionnaire (FAQ) demonstrated the most substantial weighted mean change over 12weeks, followed by the Mini-Mental State Examination (MMSE). Functional measures outperformed other measure categories. Composite scores of integrated Alzheimer's Disease Rating Scale and Clinical Dementia Rating-Sum of Boxes are more sensitive to change than their individual cognitive components, possibly driven by the functional components of the score. Neuropsychiatric measures analyzed in this study appeared to be the least sensitive in measuring progression.

Analytic Challenges in Clinical Trials of Early Alzheimer’s Disease

Background: The heavily right-skewed data seen in recently reported Alzheimer’s disease (AD) clinical trials influenced treatment contrasts when data were analyzed via the typical mixed-effects model for repeated measures (MMRM). Methods: An MMRM analysis similar to what is commonly used in AD clinical trials was compared versus robust regression (RR) and the non-parametric Hodges–Lehman estimator (HL). Results: Results in simulated data patterned after AD trials showed that imbalance across treatment arms in the number of patients in the extreme right tail (those with rapid disease progression) frequently occurred. Each analysis method controlled Type I error at or below the nominal level. The RR analysis yielded smaller standard errors and more power than MMRM and HL. In data sets with appreciable imbalance in the number of rapid progressing patients, MMRM results favored the treatment arm with fewer rapid progressors. Results from HL showed the same trend but to a lesser degree. Robust regression yielded similar results regardless of the ratio of rapid progressors. Conclusions: Although more research is needed over a wider range of scenarios, it should not be assumed that MMRM is the optimal approach for trials in early Alzheimer’s disease.

PET/CT/MRI in Clinical Trials of Alzheimer's Disease.

With the advent of PET imaging in 1976, 2-deoxy-2-[18F]fluoro-D-glucose (FDG)-PET became the preferred method for in vivo investigation of cerebral processes, including regional hypometabolism in Alzheimer's disease. With the emergence of amyloid-PET tracers, [11C]Pittsburgh Compound-B in 2004 and later [18F]florbetapir, [18F]florbetaben, and [18F]flumetamol, amyloid-PET has replaced FDG-PET in Alzheimer's disease anti-amyloid clinical trial treatments to ensure "amyloid positivity" as an entry criterion, and to measure treatment-related decline in cerebral amyloid deposits. MRI has been used to rule out other brain diseases and screen for 'amyloid-related imaging abnormalities' (ARIAs) of two kinds, ARIA-E and ARIA-H, characterized by edema and micro-hemorrhage, respectively, and, to a lesser extent, to measure changes in cerebral volumes. While early immunotherapy trials of Alzheimer's disease showed no clinical effects, newer monoclonal antibody trials reported decreases of 27% to 85% in the cerebral amyloid-PET signal, interpreted by the Food and Drug Administration as amyloid removal expected to result in a reduction in clinical decline. However, due to the lack of diagnostic specificity of amyloid-PET tracers, amyloid positivity cannot prevent the inclusion of non-Alzheimer's patients and even healthy subjects in these clinical trials. Moreover, the "decreasing amyloid accumulation" assessed by amyloid-PET imaging has questionable quantitative value in the presence of treatment-related brain damage (ARIAs). Therefore, future Alzheimer's clinical trials should disregard amyloid-PET imaging and focus instead on assessment of regional brain function by FDG-PET and MRI monitoring of ARIAs and brain volume loss in all trial patients.