Alzheimer's Disease Clinical Trials Research Articles

An unsupervised learning approach for clustering joint trajectories of Alzheimer's disease biomarkers: An application to ADNI Data.

Current models of Alzheimer's disease (AD) progression assume a common pattern and pathology, oversimplifying the heterogeneity of clinical AD. We define a syndrome as a unique biomarker progression pattern and develop a lag measure to cluster pre-dementia individuals, reflecting their pathology's multi-dimensionality. The technique uses the time-ordering of events to group individuals based on their position along the disease process and the relative positions of their markers. An application using Alzheimer's Disease Neuroimaging Initiative (ADNI) data highlights the need for our novel approach to clustering individuals into syndrome groups. Accurately characterizing biomarker curves associated with brain damage requires an initial step that groups individuals on a syndrome basis, accounting for the heterogeneity of underlying pathologies in clinical AD. Developed a novel distance measure and clustering approach for AD biomarker trajectories. Identified distinct subgroups with different biomarker progression patterns in ADNI data. Findings challenge the traditional amyloid cascade hypothesis and suggest AD heterogeneity. Clustering approach accounts for shifts in time and emphasizes progression patterns. Results have implications for AD diagnosis, targeted interventions, and clinical trials.

A multi-cohort study of longitudinal and cross-sectional Alzheimer's disease biomarkers in cognitively unimpaired older adults.

The generalizability of neuroimaging and cognitive biomarkers in their sensitivity to detect preclinical Alzheimer's disease (AD) and power to predict progression in large, multisite cohorts remains unclear. Longitudinal demographics, T1-weighted magnetic resonance imaging (MRI), and cognitive scores of 3036 cognitively unimpaired (CU) older adults (amyloid beta [Aβ]-negative/positive [A-/A+]: 1270/1558) were included. Cross-sectional and longitudinal cognition and medial temporal lobe (MTL) structural measures were extracted. Cross-sectional MTL tau burden (T) was computed from tau positron emission tomography (N=1095). We found cross-sectional tau and longitudinal structural biomarkers best separated A+ CU from A- CU. A-T+ CU had significantly faster neurodegeneration rate compared to A-T- CU. MTL tau was significantly correlated with MRI and cognitive biomarkers regardless of Aβ status. MTL tau, MRI, and cognition provided complementary information about disease progression. This large multisite study replicates prior findings in CU older adults, supporting the utility of neuroimaging and cognitive biomarkers in preclinical AD clinical trials and normal aging studies. We investigated neuroimaging and cognitive biomarkers in 3036 cognitively unimpaired (CU) participants. Medial temporal lobe (MTL) tau and longitudinal MTL atrophy best separate amyloid beta positive (A+) CU from amyloid beta negative (A-) CU. A- tau positive (T+) CU had a significantly faster neurodegeneration rate compared to A-T- CU. MTL tau correlated with structural magnetic resonance imaging (MRI) and cognition regardless of amyloid beta status. Combined baseline MTL tau, MRI, and cognition best predict Alzheimer's disease progression.

A novel longitudinal rank-sum test for multiple primary endpoints in clinical trials: Applications to neurodegenerative disorders

Neurodegenerative disorders such as Alzheimer’s disease (AD) present a significant global health challenge, characterized by cognitive decline, functional impairment, and other debilitating effects. Current AD clinical trials often assess multiple longitudinal primary endpoints to comprehensively evaluate treatment efficacy. Traditional methods, however, may fail to capture global treatment effects, require larger sample sizes due to multiplicity adjustments, and may not fully utilize the available longitudinal data. To address these limitations, we introduce the Longitudinal Rank Sum Test (LRST), a novel nonparametric rank-based omnibus test statistic. The LRST enables a comprehensive assessment of treatment efficacy across multiple endpoints and time points without the need for multiplicity adjustments, effectively controlling Type I error while enhancing statistical power. It offers flexibility for various data distributions encountered in AD research and maximizes the utilization of longitudinal data. Simulations across realistic clinical trial scenarios, including those with conflicting treatment effects, and real-data applications demonstrate the LRST’s performance, underscoring its potential as a valuable tool in AD clinical trials.

Prediction-powered Inference for Clinical Trials.

Prediction-powered inference (PPI) [1] and its subsequent development called PPI++ [2] provide a novel approach to standard statistical estimation leveraging machine learning systems to enhance unlabeled data with predictions. We use this paradigm in clinical trials. The predictions are provided by disease progression models, providing prognostic scores for all the participants as a function of baseline covariates. The proposed method would empower clinical trials by providing untreated digital twins of the treated patients while remaining statistically valid. The potential implications of this new estimator of the treatment effect in a two-arm randomized clinical trial (RCT) are manifold. First, it leads to an overall reduction of the sample size required to reach the same power as a standard RCT. Secondly, it advocates for an imbalance of controls and treated patients, requiring fewer controls to achieve the same power. Finally, this technique directly transfers any disease prediction model trained on large cohorts to practical and scientifically valid use. In this paper, we demonstrate the theoretical properties of this estimator and illustrate them through simulations. We show that it is asymptotically unbiased for the Average Treatment Effect and derive an explicit formula for its variance. An application to an Alzheimer's disease clinical trial showcases the potential to reduce the sample size.

Research Attitudes Questionnaire scores and retention in a recruitment registry.

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. To test the hypothesis that RAQ score is associated with retention behaviors in a local recruitment registry. 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. 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. 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.

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

Analytical performance assessment and suitability of the plasma β‐Amyloid 1‐40 and 1‐42 assays on the Lumipulse G instrument from Fujirebio to support Alzheimer's Disease clinical trials

AbstractBackgroundAlzheimer’s Disease (AD) is a major neurodegenerative disorder characterized by amyloid deposits in brain tissues and representing a continuously increasing global burden in need of disease‐modifying therapeutic options. Amyloid beta 1‐42 and 1‐40 peptides and the amyloid beta 1‐42/1‐40 ratio are hallmarks of AD and are commonly monitored in Cerebro‐Spinal Fluid (CSF) along other AD biomarkers, to support diagnosis and management of AD patients. Over the past few years, blood‐based AD biomarkers have emerged as highly relevant and more practical alternatives to CSF biomarkers, and further technical performance characterization of the associated assays would be beneficial to the AD research and medical community.MethodThe Lumipulse G β‐Amyloid 1‐42 and 1‐40 Plasma assays are two‐step chemiluminescent enzyme immunoassays (CLEIA) performed on the Lumipulse G system and designed for the quantitative measurement of amyloid beta 1‐42 and 1‐40 in plasma specimens. We evaluated these two assays in a CAP/CLIA environment, utilizing CLSI guidance to set validation parameters and acceptance criteria. Linearity of results, assay precision within and between analytical runs, functional sensitivity as well as other performance parameters were evaluated in endogenous samples, neat or spiked to obtain appropriate concentrations.ResultUtilizing a set of samples from apparently healthy donors above 75 years old, we were able to show an expected reference range of 154.05 – 455.50 pg/mL and 12.57 – 42.43 pg/mL for amyloid beta 1‐40 and 1‐42, respectively. Our results support a linear measurement range of 0.57 – 3,404.60 pg/mL and 0.83 – 682.00 pg/mL for amyloid beta 1‐40 and 1‐42, respectively, hence covering appropriately the expected reference range of the healthy population and allowing to capture abnormally elevated or depressed concentrations. For amyloid beta 1‐40, experiments revealed CV% ranging from 3.3 to 3.7% for intra‐ and 5.5 to 7.0% for inter‐assay precision. For amyloid beta 1‐42, CV% ranged from 3.4 to 5.4% for intra‐ and 5.8 to 8.8% for inter‐assay precision.ConclusionOverall, these assays demonstrate technical robustness and reliability to support clinical trials for AD in the plasma matrix, which can offer a less invasive, yet valuable test to enrich for amyloid positivity in neurodegenerative clinical trials.

Utility of plasma p‐tau217 as a secondary outcome for clinical trials across AD continuum

AbstractBackgroundBlood‐based biomarkers offer a cost‐effective and simple alternative for clinical use in the context of Alzheimer's disease (AD). It has already been shown that plasma phosphorylated tau at threonine 217 (p‐tau217) is associated with amyloid (Aβ), neurofibrillary tau tangles, and cognitive decline. Longitudinal studies confirmed its ability to track AD progression. Therefore, the aim of this study is to evaluate the potential utility of changes in plasma p‐tau217 as secondary outcome in clinical trials across the AD spectrum.MethodWe included 189 individuals with available plasma p‐tau217 measurements at two time points (24 months follow‐up) across two cohorts:[MYHAT‐NI, population‐based(USA): 51 Cognitively Unimpaired(CU); BICWALZS, memory clinic(South Korea): 135 Cognitively Impaired(CI) individuals], all with baseline Aβ‐PET. Plasma p‐tau217 was quantified using the ALZpath assay. We used linear regression to investigate p‐tau217 association with cognitive decline. Effect size was determined by biomarker mean change divided by the standard deviation. We also estimated the sample size required to detect a 25% drug effect on p‐tau217 reduction with 80% power at a significance level of 0.05.ResultBaseline and longitudinal changes in p‐tau217 were significantly associated with cognitive decline independently of Aβ status in both CU and CI individuals (Table 1). Functionally, individuals classified with mild dementia (CDR‐SB&gt;4.5) showed greater and pronounced annual changes in p‐tau217 compared to the other groups (Figure 1A). Notably, Aβ+ individuals presented a higher effect size for p‐tau217 changes in both CU(0.79) and CI(0.74) groups, compared to the whole population (Figure 2B). The sample size required for a clinical trial including of Aβ+ individuals were 1,276 for CU and 940 for the CI. Cost analysis revealed that in trials recruiting CU Aβ+ individuals targeting tau pathology using p‐tau217 as a secondary outcome was 1.5, 3.7, and 3.9 times more cost‐effective than 18F‐MK‐6240 tau‐PET, 18F‐Flortaucipir tau‐PET, and p‐tau181, respectively (Figure 2). Similarly, in trials with CI Aβ+ participants, p‐tau217 was 1.5 times less expensive than [18F]‐MK‐6240 tau‐PET, and 3.7 times less than [18F]‐Flortaucipir tau‐PET.ConclusionOur results demonstrated that changes in plasma p‐tau217 is associated with cognitive decline across the AD continuum. Furthermore, p‐tau217 shows promise as a secondary outcome in AD clinical trials.

Cryo‐EM structures of amyloid beta and tau filaments in down syndrome

AbstractBackgroundDown syndrome (DS) is the most common and best‐known chromosomal disorder in humans and the most frequent cause of intellectual disability of genetic origin, affecting about 6 million people worldwide. Individuals with DS may develop Alzheimer disease (AD) by age 55‐60 years, and sometimes as young as 40 years due to the triplication of the amyloid β precursor protein (AβPP) gene, which is located on chromosome 21. The AD neuropathological phenotype in DS includes amyloid‐β (Aβ) deposition (parenchymal and vascular) and neurofibrillary tangles comprised of tau protein. Aβ peptide species ending at 42 (Aβ42) are the main component of senile plaques and diffuse deposits in AD and AD in DS, while Aβ peptides ending at position 40 (Aβ40) are the predominant Aβ peptides found in both leptomeningeal and cortical vessels. Whether there is a difference in the structures of Aβ and tau filaments between AD and AD in DS, is unknown.MethodWe used cryo‐electron microscopy (cryo‐EM) to study the structure of Aβ and tau filaments extracted from the brains of two individuals with DS. Both individuals had been clinically diagnosed with AD, which was neuropathologically confirmed.ResultWe found two types of Aβ42 filaments (I and II) identical to those found in sporadic and familiar AD and two novel Aβ40 filaments (type IIIa and IIIb) that differ from those previously reported in AD. Tau filaments (paired helical filaments, PHFs, and straight filaments, SFs), were identical to those from AD and related diseases.ConclusionThis cryo‐EM study emphasizes the similarities and differences between amyloid filaments in AD and AD in DS. The relevance of the structural differences between Aβ40 filaments in cerebral amyloid angiopathy in AD and DS is unknown. Further research is needed to determine whether type IIIa and IIIb Aβ40 filaments are unique to DS. Tau filaments (PHFs and SFs) were identical to those in AD, supporting the notion of a common mechanism through which amyloids trigger aggregation of tau. This knowledge is crucial for understanding AD in individuals with DS and assessing whether adults with DS could be included in AD clinical trials.

Quantitative‐ and visual read‐based flortaucipir PET T+ criteria for the pathophysiological assessment of Alzheimer’s Disease

AbstractBackgroundThe revised NIA‐AA biological Alzheimer’s disease (AD) staging scheme considers various tau positivity (T+) rules based on global and regional neocortical tau PET uptakes. In AD clinical trials, various tau PET criteria (e.g., visual+quantitative [TauVQ], Mintun et al., 2021) have been employed to select eligible patients. In this study, we compared six T+ criteria in terms of their potential usefulness for biological staging and clinical trial eligibility.MethodsN=296 symptomatic (189/107 MCI/AD‐dementia) subjects from AV‐1451‐A05 (NCT02016560) were included. Six different tau‐PET stratifications (T+/T‐) were employed: Two quantitative global neocortical uptake measures (CenTauR‐Universal, Villemagne et al., 2023, and AD‐signature weighted neocortical, Devous et al., 2018); two quantitative temporal‐based uptake measures (CenTauR‐Meta‐temporal, Villemagne et al., 2023 and Early Tau VOI, Kotari et al., 2023), a visual read‐based method (Fleisher et al., 2021) and TauVQ method. A dataset of young cognitively normal subjects (N=16) was utilized to determine T+ thresholds for quantitative measures (mean+2.5xSD SUVr). The tau stratifications were evaluated for their agreement with amyloid‐PET positivity and for AD‐related prognostic utility using signal‐to‐noise ratios (SNRs) of T+ for (1) Annualized change in AD Assessment Scale‐Cognitive (ADAS‐Cog11) and (2) 18‐month change in CenTauR‐Meta‐temporal SUVr.ResultsCenTauR‐Universal and CenTauR‐Meta‐Temporal derived T+ groups showed comparable SNRs (Figure 1A‐B) and agreement with amyloid positivity (Figure 2). The TauVQ‐ and the visual read‐derived T+ groups also performed similarly. The regional early tau VOI‐based T+ group, compared to the global AD‐signature neocortical‐based T+ group, showed a higher SNR for 18‐month change in CenTauR‐Meta‐temporal SUVr. However, a lower SNR for annualized ADAS‐Cog11 change and a lesser agreement with amyloid positivity were observed. Comparisons using PET‐to‐autopsy cohort will be presented.ConclusionsOur analyses suggest using CenTauR‐Meta‐temporal and visual read‐based regional T+ criteria may identify a larger subgroup of symptomatic patients exhibiting similar amyloid positivity prevalence and clinical decline compared to those determined by corresponding global T+ criteria. Among examined methods, the early tau VOI‐based T+ criterion possibly captures the earliest pathologic stage. Lastly, CenTauR‐based T+ groups showed lower SNRs for tau and clinical progression, suggesting flortaucipir‐specific criteria might be more sensitive for prognostic evaluations. Further validations using different datasets and cut points are warranted.

Early Identification of Alzheimer’s Disease with a Machine Learning‐Enabled Digital Cognitive Assessment: Concurrent Detection of Cognitive Impairment and Amyloid‐Beta PET Status

AbstractBackgroundMaximizing the benefits of disease‐modifying treatments (DMTs) for Alzheimer’s disease (AD) requires early identification of cognitive impairment and abnormal brain amyloid‐beta (Aβ) status. Either one alone is insufficient. Additionally, clinical trials of DMTs are impeded by high screen failure rates and costly prescreening. Thus, an efficient and cost‐effective solution to streamline the process of early AD identification is urgently needed. This study aimed to assess the accuracy of a brief digital cognitive assessment, the Linus Health Digital Clock and Recall (DCR), to identify cognitive impairment and predict brain Aβ status.Method930 participants (mean age 72.0±6.7; 56.8% female; 23% minorities) in the Bio‐Hermes‐001 study were classified as cognitively unimpaired, mild cognitive impairment, or probable Alzheimer’s dementia, and 35.1% were Aβ+ on 18F‐florbetapir PET scan. A DCR‐based algorithm (LinusAD) used age, APOE status, drawing metrics, speech and acoustic features, and temporal‐spatial features of stylus manipulation. LinusAD was compared with MMSE and blood‐based biomarkers (BBMs) Lilly pTau‐217, Quanterix pTau‐181, C2N Aβ42/40, and C2N Amyloid Probability Score (APS). Superiority or non‐inferiority was established if 95% confidence interval of the bootstrapped AUC difference between two tests was higher than or within ΔAUC±0.1.ResultCognitive‐impairment identification by the DCR (AUC=0.85) was superior to Aβ42/40, pTau‐181, and pTau‐217 (AUCs=0.63, 0.66, 0.72) and non‐inferior to RAVLT and MMSE (AUCs=0.89, 0.82). Aβ‐status prediction by LinusAD (AUC=0.882) was equivalent to BBMs (AUC range: 0.775–0.887; average AUC=0.82) and superior to MMSE (AUC=0.71) (see Table and Figure). Combining the DCR with Aβ42/40, pTau‐181, and pTau‐217 improved their Aβ‐status prediction performance to AUCs of 0.882, 0.835, and 0.905, respectively.ConclusionThe 3‐minute DCR was superior to BBMs in detecting cognitive impairment and boosted their Aβ‐PET prediction ability to levels comparable to CSF biomarkers of AD. Digital cognitive assessments that leverage AI process metrics, such as the DCR, enable cost‐effective integration into multi‐step clinical workflows to prioritize the most suitable patients for BBM testing, DMTs, and decrease high screen‐failure rates in AD clinical trials.

(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