AD Trials Research Articles

Automated remote speech-based testing of individuals with cognitive decline: Bayesian agreement of transcription accuracy.

We investigated the agreement between automated and gold-standard manual transcriptions of telephone chatbot-based semantic verbal fluency testing. We examined 78 cases from the Screening over Speech in Unselected Populations for Clinical Trials in AD (PROSPECT-AD) study, including cognitively normal individuals and individuals with subjective cognitive decline, mild cognitive impairment, and dementia. We used Bayesian Bland-Altman analysis of word count and the qualitative features of semantic cluster size, cluster switches, and word frequencies. We found high levels of agreement for word count, with a 93% probability of a newly observed difference being below the minimally important difference. The qualitative features had fair levels of agreement. Word count reached high levels of discrimination between cognitively impaired and unimpaired individuals, regardless of transcription mode. Our results support the use of automated speech recognition particularly for the assessment of quantitative speech features, even when using data from telephone calls with cognitively impaired individuals in their homes. High levels of agreement were found between automated and gold-standard manual transcriptions of telephone chatbot-based semantic verbal fluency testing, particularly for word count.The qualitative features had fair levels of agreement.Word count reached high levels of discrimination between cognitively impaired and unimpaired individuals, regardless of transcription mode.Automated speech recognition for the assessment of quantitative and qualitative speech features, even when using data from telephone calls with cognitively impaired individuals in their homes, seems feasible and reliable.

A data-driven approach for revealing the linkages between differences in electrochemical properties of biochar during anaerobic digestion using automated machine learning

Biochar is commonly used to enhance the anaerobic digestion of organic waste solids and wastewater, due to its electrochemical properties, which intensify the electron transfer of microorganisms attached to its large surface area. However, it is difficult to create biochar with both high conductivity and high capacitance, which makes selecting the right biochar for engineering applications challenging. To address this issue, two Auto algorithms (TPOT and H2O) were applied to model the effects of different biochar properties on anaerobic digestion processes. The results showed that the gradient boosting machine had the highest predictive accuracy (R2 = 0.96). Feature importance analysis showed that feedstock concentration, digestion time, capacitance, and conductivity of biochar were the main factors affecting methane yield. According to the two-dimensional (2D) partial dependence plots, high-capacitance biochar (0.27–0.29 V·mA) is favorable for substrates with low-solid content (< 19.6 TS%), while the high-conductivity biochar (80.82–170.58 mS/cm) is suitable for high-solids substrates (> 20.1 TS%). The software, based on the optimal model, can be used to obtain the ideal range of biochar for AD trials, aiding researchers in practical applications prior to implementation.

Streamlining and standardizing logistical, administrative, and regulatory workflows for sharing data from AD trials

AbstractFailure to share data successfully and quickly results in the waste of scientific resources as experiments are erroneously repeated or future resources are misallocated. Successful data sharing maximizes prior public and private investment of time, personal and cost in AD trials. To date, training resources regarding general regulatory requirements for managing data are widely available, however trainings addressing common logistical and administrative decision‐making and providing administrative strategies in the context of data sharing by AD trial teams and sponsoring partners are rare or too rigid to be broadly applicable. A widely available and well‐known training and education resource will provide a coherent systematic approach for multi‐stakeholder research projects that intend to share data publicly. Such projects are at increased risk of delay or mismanagement and a cohesive effort to generate flexible, streamlined modules offering repeatable solutions for multi‐stakeholder AD research data sharing would benefit the research team as well as the requesting investigators. AD research staff should rapidly develop familiarity and a common understanding of the practices and policies of data sharing so that this aspect of research similarly streamlined to other research practices such as informed consent, data privacy, etc. Attendees will participate in a discussion establish a priori principles for charting a course for establishing broad competence in data sharing by combining principles of scientific rigor with a synthesis of best practices for logistical, scientific, and regulatory workflows.

Alternative Endpoints and Models for AD Prevention Trials

AbstractBackgroundThere are several challenges for AD prevention trials. First, the subtle cognitive change in asymptomatic disease stages requires large sample sizes, e.g. 400‐500/arm for two phase 3 confirmatory trials, or 800‐900/arm for a single phase 3 confirmatory trial, using the traditional MMRM. Second, large, international trials may necessitate the use of different tracers for imaging biomarker assessment due to global availability issues, however, statistical models that harmonize tracer differences within clinical trials are still lacking. Third, primary prevention trials using time to event endpoint demands even larger sample sizes, e.g., 1650/arm for TRAILBLAZER‐ALZ 3 with time‐to‐first‐conversion from CDR global 0 to &gt;0 as the outcome. The percentage slowing relative to placebo decline (e.g., lecanemab led to 27% slowing at 18 months) is a popular concept to describe treatment effect. Instead of estimating the absolute difference using MMRM, we proposed to model this percentage treatment effect directly using the percentage or proportional MMRM (pMMRM). pMMRM can harmonize assessments from different tracers by converting the absolute difference into a shared proportional treatment effect across tracers. When using time‐to‐conversion as the primary outcome, instead of using a single event, Cox proportional hazards models for recurrent events can capture the treatment effect after the first event, where a recurrent event is defined as any increment over the previous visit in CDR global.MethodWe first demonstrated that disease modifying treatment effects are essentially proportional retreatment effects. Then, we evaluated the power advantage of pMMRM over MMRM and Cox recurrent events model over single event model through simulations. Finally, we demonstrated how to harmonize data from different tracers using pMMRM.ResultFigure 1 demonstrates that disease modifying treatment effects are like proportional treatment effects, leading to diverged disease progression trajectories and increased absolute differences. Figure 2 shows the power comparison between MMRM and pMMRM. pMMRM led to a power increase up to 30% over MMRM.ConclusionAlternative models such as pMMRM or Cox recurrent event models can yield greater power and pMMRM can harmonize assessment from different tracers, which may help enable global prevention trials in AD with disease modifying drug effects.

PTau181 plasma biomarker performance as an inclusion criterion for Alzheimer’s Disease clinical trials

AbstractBackgroundAdvances in ultrasensitive detection techniques for blood biomarkers allow the quantification of AD‐specific phosphorylated Tau proteins, including Tau phosphorylated at threonine 181 (pTau181) in patient plasma. pTau blood‐based biomarker have shown great promise as inclusion criteria and secondary endpoint evaluation in clinical trials.MethodThe University of British Columbia (UBC) CARD biobank plasma samples from clinically diagnosed AD and non‐AD patients were used to establish clinical and analytical validity of the pTau181 plasma assay per FDA fit‐for‐purpose guildelines (Neurcode USA, Inc.). We assessed the analytical measurement interval, clinical reportable range, linearity, intra‐laboratory precision, specimen stability, interference, and clinical performance.ResultThe pTau181 plasma assay provides a robust and accurate biomarker approach for independent determination of AD, with an AUC of 0.9 in our validation study cohort. The cut‐point (≥ 30 ng/L) had 100% sensitivity and 88% specificity for AD diagnosis in autopsy‐confirmed samples. The plasma pTau181 assay appears to be performing well as additional screening metric for inclusion of mild‐to‐moderate AD subjects in Phase 3 clinical trials (i.e. RETHINK‐ALZ and REFOCUS‐ALZ). 89% of the clinical sites participating in the REFOCUS‐ALZ study and 80% of sites participating in the RETHINK‐ALZ had at least 70% of screened subjects meeting this criterion.ConclusionAD plasma biomarker (pTau181) quantified using an RUO assay has great potential both as a diagnostic tool and to streamline clinical trials in AD.

Clinical Manifestations.

Accelerated Long-term Forgetting (ALF) is the phenomenon whereby material is retained normally over short intervals (minutes or hours) but forgotten abnormally rapidly over longer periods (days or weeks). ALF may be an early marker of cognitive decline, but little is known about its relationships with preclinical Alzheimer's disease pathology in older adults, and how memory selectivity may influence which material is forgotten. Participants in 'Insight 46', a sub-study of the MRC National Survey of Health and Development (British 1946 birth cohort), completed cognitive and neuroimaging assessments at two time-points (baseline at age ∼70; follow-up ∼2.4 years later). At follow-up, we assessed Complex Figure Drawing (copy; immediate recall; 30-minute recall; 7-day recall). Complex Figure items were categorized as 'outline' or 'detail' (Fig1), to test the hypothesis that forgetting the outline of the structure would be more sensitive to the effect of brain pathologies. ALF scores were calculated as the proportion of material retained after 7 days, relative to 30 minutes. Rates of cerebral atrophy between baseline and follow-up were quantified from T1-weighted MRI using the Brain Boundary Shift Integral (BBSI). β-amyloid status (positive/negative) was determined from 18F-Florbetapir-PET. Baseline serum neurofilament light (NfL) was quantified (Quanterix Simoa assay). Multivariable regression models were used to investigate the effects (mutually adjusted) of β-amyloid status, BBSI and NfL on ALF in n = 316 clinically-normal individuals (50% female; 22% β-amyloid positive; 30% APOE-ε4 carriers), and to explore interactions between these predictors, adjusting for potential confounders including prospectively-collected childhood cognitive ability and education. 'Outline' items were better retained than 'detail' (Fig1). β-amyloid-positive participants had poorer ALF scores for 'outline' (but not 'detail') items (Fig1C; Table 1). Unexpectedly, higher NfL was associated with scores for 'outline' items (Table 1). Greater rate of cerebral atrophy predicted poorer retention among participants with elevated β-amyloid and higher NfL (Table 1; Fig2). These results provide evidence of associations between biomarkers of brain pathologies and ALF in 73-year-olds. Interactions between different biomarkers merit further exploration. ALF may be a sensitive outcome measure for therapeutic trials in preclinical AD. Better retention of 'outline' (vs. 'detail') items illustrates the strategic role of memory selectivity.

DDR1 regulates CNS collagen IV expression and vascular fibrosis in AD to improve CNS vascular integrity

AbstractBackgroundBlood‐brain barrier breakdown, a feature associated with AD pathology and treatments, was examined as a potential therapeutic target in both genetic mouse models of AD and clinical trial data from human patients with AD treated with nilotinib.MethodsDDR1 knockout mice crossed with TgAPP mice were probed for levels of CNS collagen to examine the effects of DDR1 in vivo. CSF samples obtained from AD subjects treated with either nilotinib (n = 12) or placebo (n = 11) for 12 months were analyzed using whole genome RNA sequencing; significantly altered miRNA expression was examined for potential downstream effects regulating neurovascular integrity.ResultsCompared to TgAPP mice, staining of CNS sections for collagen IV in TgAPP/DDR1 knockout mice revealed decreased vascular collagen. CSF obtained from AD subjects treated with nilotinib revealed increased levels of miRNAs regulating collagen production, suggesting decreased vascular fibrosis and neuroinflammation, and improved neurovascular integrity.ConclusionDDR1 inhibition with nilotinib may improve neurovascular integrity in AD by reducing CNS collagen IV, vascular fibrosis, and neuroinflammation. Nilotinib should be considered as an adjunctive therapy to approved treatments ‐ including anti‐amyloid antibodies ‐ for individuals with AD.

Lecanemab phase 3 clarity AD trial: ARIA with the use of antiplatelets or anticoagulants in early Alzheimer's disease

Lecanemab phase 3 clarity AD trial: ARIA with the use of antiplatelets or anticoagulants in early Alzheimer's disease

Interpretable Deep Learning and MultiOme data repurposing in Alzheimer’s target and drug discovery: NIA Artificial Intelligence and Machine Learning Consortium

AbstractBackgroundHigh‐throughput DNA/RNA sequencing technologies have rapidly led to a robust body of genetic and genomic data in the Alzheimer’s Disease Sequencing Project (ADSP); however, approaching AD with a simplistic single‐target approach has been demonstrated effective for developing symptomatic therapies but ineffective when attempted for disease modification. Therapeutic approaches by specifically modulating genetic risk genes are essential for development of disease‐modifying treatments in AD. However, existing data, including genetics, genomics, transcriptomics, proteomics, and interactomics, have not yet been fully utilized and integrated to explore the roles of targeted therapeutic development for AD.MethodWe will introduce how our team combines tools from artificial intelligence (AI), network medicine, endophenotype models, multi‐omics (genomics, transcriptomics, and proteomics), and electronic health records (EHRs), to identify potential drug targets and repurposable drugs for AD. The fundamental premise of our method is that AD risk genes exhibit distinct functional characteristics compared to non‐risk genes and, therefore, can be distinguished by their aggregated human brain‐specific functional genomic features from various quantitative trait loci (X‐QTL), including expression QTL (eQLT), protein QTL (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL).ResultVia applying our deep learning model to the latest AD GWAS data, we identified 156 putative AD‐risk genes that were differentially expressed in AD‐associated microglia and astrocytes from large single‐nuclei RNA‐sequencing data of human postmortem brains with AD. Combining network‐based prediction and retrospective case‐control observations with 10 million individuals, we identified that usage of four drugs (ibuprofen, gemfibrozil, cholecalciferol, and ceftriaxone) is significantly associated with reduced likelihood of AD incidence, after adjusting for various confounding factors. Importantly, gemfibrozil (an approved lipid regulator) is significantly associated with reduced risk of AD compared to simvastatin (another approved anti‐lipid medicine under Phase II AD trials), using an active user design (odds ratio 0.57, 95% CI 0.51‐0.63, P &lt; 0.0001).ConclusionFrom a translational perspective, the deep learning and network‐based approaches presented here, if broadly applied would significantly catalyze effective treatment development in AD and other neurodegenerative diseases from the human genome sequencing findings.

Statistical innovations in the API ADAD Colombia trial: a randomized, double‐blind, placebo‐controlled, parallel‐group study in cognitively unimpaired PSEN1 E280A mutation carriers evaluating efficacy and safety of crenezumab

AbstractBackgroundA number of extended families in Colombia with early‐onset ADAD have a PSEN1 mutation at codon 280 (E280A), comprise the largest ADAD kindred in the world. Trial participants have been treated with crenezumab (an experimental anti‐amyloid monoclonal antibody) or placebo. Topline results were presented in August, 2022 at AAIC. Although the trial did not demonstrate a statistically significant clinical benefit in either of its co‐primary endpoints, multiple statistical innovations were incorporated into this trial.MethodThe API ADAD Colombia trial is a randomized, double‐blind, placebo‐controlled, parallel‐group phase 2b study evaluating the efficacy, safety, and tolerability of crenezumab in cognitively unimpaired ADAD PSEN1 E280A mutation carriers in Colombia (NCT01998841). Due to under enrollment and concerns with the sensitivity of the primary endpoint, the protocol was amended mid‐trial to change from 260 weeks of treatment to a common‐close design whereby treatment continued in the double‐blind portion until the last enrolled participant received 260 weeks of treatment. Because of the change in trial design and power considerations, the primary analysis was adapted from a traditional mixed model with repeated measures (MMRM) based analysis to a random coefficient regression model (RCRM) based analysis. To quantitatively examine the adequacy of the RCRM assumptions, a robust variance estimator (Huber 1967; White 1982) was prespecified as a sensitivity analysis to calculate the standard errors and p‐values of the co‐primary endpoints. This study used a type‐I‐error‐controlled statistical procedure to identify and elevate the Free and Cued Selective Reminding Test (FCSRT) cueing index to a co‐primary endpoint. To comprehensively assess the crenezumab effect across primary and secondary endpoints, the prespecified relative reduction point estimates along with the bootstrapped 95% confidence intervals were generated.ResultInnovative statistical methods in this trial will be presented.ConclusionAlthough the API Colombia study did not meet its co‐primary endpoints, statistical learnings from this trial can benefit development of future AD trials. To further maximize the value of this unique trial dataset, participant‐level data will be made publicly available, including clinical, imaging, and biomarker data.Funding: NIA grants RF1‐AG041705, R01‐AG055444, Genentech, Roche, philanthropic contributions to Banner Alzheimer’s Foundation. FlorbetapirPET was partly supported by Avid and Lilly.

A perspective on data sharing from potentially registrational trials

AbstractIt is well recognized that sharing rich and rigorously collected clinical trial data with researchers who were not conducting the study has the potential to accelerate progress in Alzheimer’s disease.. Such data sharing can contribute to an understanding of pathophysiology, natural history, performance of outcome measures, and the profile of response to treatments, and can help researchers to design better studies and health authorities and payors to evaluate the benefits of therapies. The complexities involved in such data sharing include the need to keep up with evolving patient privacy laws, protecting participant anonymity, ensuring trial integrity, and balancing regulatory and publication requirements. In 2012 Roche/Genentech made a commitment to Banner Alzheimer’s Institute and the NIH to share baseline and longitudinal trial data from the Alzheimer’s Prevention Initiative Autosomal Dominant AD trial, a public‐private partnership. We will describe the mechanisms developed to share data from the API ADAD Trial (baseline data now available; longitudinal clinical, imaging, and biomarker data to follow) including methods employed to protect confidentiality and anonymity of participants, and the process for requesting and accessing baseline data. We will also offer learnings from our experiences in sharing data from other neurodegeneration trials.The API ADAD trial was funded by NIA grants RF1‐AG041705, R01‐AG055444, Genentech, Roche, philanthropic contributions to Banner Alzheimer’s Foundation. Florbetapir PET was partly supported by Avid and Lilly.

Xanamem shows pro‐cognitive activity with clinically meaningful effect sizes across 3 independent, placebo‐controlled clinical trials

AbstractBackgroundXanamem® is a brain‐penetrant inhibitor of 11‐beta hydroxysteroid dehydrogenase type 1, which converts intracellular cortisone to cortisol and is highly expressed in brain regions such as the hippocampus. Elevated plasma and CSF cortisol is strongly associated with cognitive decline.MethodEffects of Xanamem on cognition were assessed in 3 independent, placebo‐controlled, double‐blind trials. The XanaHES (n = 42, 20 mg) and XanaMIA (n = 105, 5 &amp; 10 mg) Phase 1b trials used the computerised Cogstate system to assess cognition in normal, older volunteers. The XanADu‐X biomarker extension study (n = 72, 10 mg) explored clinical and cognitive outcomes in subgroups (n = 34 each) of the XanADu Phase 2a AD trial with higher (H) or lower (L) plasma p‐Tau181 in a new prospective analysis. To compare the results across trials, we focus on the cognitive effect sizes measured by the Cohen’s statistic (treatment effect vs. placebo / pooled baseline standard deviation).ResultXanamem was safe and well‐tolerated with no treatment‐related SAEs and the predominantly mild AEs were generally equally distributed between Xanamem and placebo. In XanaHES and XanaMIA, a pattern of clinically significant improvements was observed in attention and working memory compared to placebo in the Xanamem groups, with Cohen’s d ranging up to 1.27. Improvements from baseline were observed in both Xanamem and placebo in domains of episodic memory and executive function without a treatment difference. In XanADu, Xanamem demonstrated clinically significant benefit with a Cohen’s d of 0.41 on the CDR‐SB compared to placebo in the H group but not in the L group. In both L and H groups improvements in executive function were seen favouring Xanamem (Cohen’s d = 0.34 and 0.26, respectively) and the MMSE (Cohen’s d = 0.32 and 0.16, respectively). There were no improvements observed on original XanADu primary outcome measures of ADAS‐Cog14 and ADCOMs.ConclusionXanamem displays activity in multiple domains of cognition including attention, working memory, and executive function with clinically meaningful effects in normal subjects and in patients with p‐Tau‐elevated mild AD. These data suggest Xanamem may be both a pro‐cognitive drug and disease‐modifying agent and guide design of future trials.

Integrated AD trial data from multiple sources and stakeholders

AbstractThere is every reason to cheer recent advances is treatments and diagnostics for Alzheimer’s and other dementias, but enormous need remains, and progress is still too slow. An important way to accelerate new discoveries is through sharing of the vast amounts of relevant data, especially the data collect from human research participants. There is consensus among many researchers, clinicians, patients, and governments that data sharing accelerates science by increasing the utility of data. This allows for new analysis, meta‐analysis, and replication, inspiring new ideas and approaches.There are, however, challenges to data sharing. These include accessible and affordable infrastructure, tools, and resources to support data curation, sharing, and analysis. There also needs to be more incentives – to move away from siloed efforts and towards global collaboration.The Alzheimer’s Disease Data Initiative (ADDI) is dedicated to data sharing by advancing solutions to these challenges. The AD Workbench provides researchers with the infrastructure to share data and to discover data within a secure, cloud‐based environment.For data contributors, supports and resources are available to assist with harmonization and curation of data. There are also options available regarding permissioned access, whether the data is simply accessed on the AD Workbench or through federated sharing. For researchers, the AD Workbench offers compute resources, data analysis tools, and other resources to help advance their work.To both support work on the AD Workbench and to foster community and collaboration, AD Connect offers resource libraries, discussion forums, and other helpful information. We know that meaningful ADRD advances will not be made by any one person or organization. Bringing researchers together, to engage, interact, and inspire each other will help us move closer to fundamentally transforming ADRD research.

PK/PD analyses of ARIA‐E and isolated ARIA‐H in Lecanemab Clarity AD study

AbstractBackgroundClinical efficacy of lecanemab was confirmed in a Phase 3 trial (Clarity AD, NCT03887455), reducing markers of amyloid in early Alzheimer’s disease and slowing cognitive and functional decline as compared to placebo. However, some subjects experienced amyloid‐related imaging abnormalities (ARIA) associated with edema/effusion (ARIA‐E) or hemorrhage (ARIA‐H). PK/PD analyses were performed to explore the potential relationship between ARIA‐E and isolated ARIA‐H incidences with lecanemab exposure and key covariates.MethodThe Clarity AD trial randomized 1795 participants to receive lecanemab (N = 898, 10 mg/kg bi‐weekly) or placebo (N = 897). A total of 177 ARIA‐E incidences were observed from pooled Phase 2 (NCT01767311) and Phase 3 (Clarity AD) data. Additionally, a total of 150 participants from Clarity AD trial experienced treatment‐emergent isolated ARIA‐H (an ARIA‐H event without concurrent ARIA‐E). The ARIA‐E incidence rate was modeled as a function of lecanemab steady state exposures, and effects of potential covariates were explored. Graphical PK/PD analysis was performed to examine the relationship between isolated ARIA‐H and lecanemab steady‐state exposures.ResultThe observed ARIA‐E incidence was best modeled using lecanemab maximum concentration at steady‐state (Css,max) with ApoE4 genotype identified as a significant covariate. The model indicates a higher ARIA‐E incidence in homozygous carriers compared to non‐homozygous carriers or noncarriers (p&lt;0.0001). At mean lecanemab Css,max = 305 µg/ml in subjects receiving 10 mg/kg bi‐weekly in Clarity AD, the model predicted a 5.45% (95% CI: 3.75% ‐ 7.84%) incidence of ARIA‐E in noncarriers, 9.85% (95% CI: 7.96% ‐ 12.1%) in heterozygous, and 28.0% (95% CI: 22.6% ‐ 34.1%) in homozygous carriers. No other covariate explored (age, sex, race, baseline MMSE, or occurrence of anti‐drug antibodies) had a statistically significant effect on ARIA‐E incidence. For isolated ARIA‐H, the graphical analysis identified no apparent relationship between isolated ARIA‐H incidence with lecanemab exposure. Isolated ARIA‐H incidence was associated with ApoE4 genotype, with homozygous having the highest isolated ARIA‐H incidence.ConclusionThe incidence of ARIA‐E increased with lecanemab Css,max. The incidence rate of isolated ARIA‐H was demonstrated independent of lecanemab exposure. The ApoE4 genotype impacts both ARIA‐E and isolated ARIA‐H, with increased incidences in homozygous carriers compared to heterozygous carriers or non‐carriers.

85 Performance Consistency on a Measure of Sustained and Selective Attention

Objective:Attention concerns, particularly difficulties with focusing and regulating attention, are reported in diverse clinical contexts. The Ruff 2&amp;7 Selective Attention Test (Ruff 2&amp;7; Ruff &amp; Allen, 1996) is a measure of sustained and selective attention that assesses automatic detection and effortful processing. The goal of this study was to create an internal consistency metric within this test and to determine cognitive predictors by evaluating associations with executive control of attention and other cognitive skills. It was hypothesized that those who are more consistent across Ruff 2&amp;7 performance would have more robust executive functioning skills, particularly those related to regulating and directing attention and the planning and utilization of cognitive resources.Participants and Methods:The current study examined a clinical sample of 98 United States veterans with a history of mild traumatic brain injury. After excluding invalid cases (n=24), the final sample consisted of 74 veterans (Age=38.5 (8.9) years old; 13.9 (2.2) years of education; 78% male; 82% white, 7% Black, 8% Hispanic, 2% Asian). A consistency score was defined as the absolute value of the intertrial change in target hits plus errors across each pair of trials of the same stimulus type (Automatic Detection, AD, and Controlled Search, CS). Hierarchical linear regression modeling was used to evaluate the relative contributions of memory and executive functions (Rey Auditory Verbal Learning Test, Delis-Kaplan Executive Function System Tower Test, phonemic fluency, Trail Making Test B) and subjective symptom report (PTSD Checklist for DSM-5, Barkley Adult ADHD Rating Scale for DSM-IV).Results:The mean deviation scores for the two trial types were similar (AD mean=13.6, SD=5.9; CS mean=13.6, SD=5.3). In predicting consistency across AD trials, delayed recall contributed 11% unique variance (p=.013), while no other block was statistically significant. For CS trials, self-reported PTSD and inattention symptoms contributed a combined 20% of unique variance to the model (p=.007), while there were no statistically significant cognitive predictors in this model.Conclusions:Contrary to expectation, executive function measures did not explain statistically significant variance in performance across either trial type. Less consistent performance on AD trials was associated with weaker verbal memory. Less consistent performance on CS trials, which theoretically require greater executive control, was not associated with any cognitive scores, but was associated with more severe self-reported psychological and inattention symptoms. These findings buttress the conceptual distinction between AD and CS trial types, and they point to both cognitive and non-cognitive underpinnings of performance consistency.

Prediction of brain amyloidosis: a multi‐site machine learning analysis

AbstractBackgroundPosition emission tomography (PET) imaging and cerebrospinal fluid (CSF) analysis are the gold standards for evaluating brain amyloidosis in vivo, but their utility is limited by invasive procedures, high costs of PET, and limited accessibility to PET facilities. Prior research has used machine learning to assess amyloidosis based on non‐invasive and easily accessible information such as cognitive test scores and genetic data, but past accounts have typically used a single cohort to train and test their models, which may undermine their generalizability. Importantly, most studies have imputed amyloidosis of symptomatic participants without evaluating their models on cognitively unimpaired (CU) individuals. Accordingly, we aimed to develop algorithms that estimate risks for amyloidosis and validate them on CU participants from an external cohort.MethodData from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (n = 987) and the Charles F. and Joanne Knight Alzheimer Disease Research Center (Knight ADRC) (n = 688) were used. The examined predictors for amyloidosis were demographic factors, body mass index (BMI), apolipoprotein E (APOE) genotype, and performance on cognitive tests (Table 1). Random forest models were trained with nested cross‐validation (CV) on the ADNI cohort before testing on the Knight ADRC cohort for a reliable assessment of generalizability. The outer CV loop enabled unbiased measurement of prediction performance, and the inner loop hyperparameter optimization.ResultThe model achieved an area under the receiver operating characteristic curve (AUC) of 0.81 [0.79, 0.84] on the ADNI cohort (Table 2), and its performance generalized well to the Knight ADRC participants. When only CU participants are considered, performance was overall lower than that for the full cohort, which we expect given that they have less over pathology. The model was nonetheless able to achieve an AUC of 0.78 [0.75, 0.81] on the Knight ADRC CU participants.ConclusionMachine learning models can predict brain amyloidosis with a moderate degree of accuracy on asymptomatic individuals and generalize well to external cohorts. Our models can be used to screen for individuals likely to be Aβ PET‐positive, thus reducing preclinical AD trial recruitment cost by avoiding unnecessary PET scans.

Effectiveness and Safety of Dabigatran Reversal with Idarucizumab in the Taiwanese Population: A Comparison Based on Eligibility for Inclusion in Clinical Trials.

Background and Objectives: The effectiveness and safety of idarucizumab for the reversal of the effects of dabigatran have been proven. However, there remains a paucity of literature comprehensively investigating outcomes in real-world patients. This is especially true when comparing patients who were eligible for inclusion in the RE-VERSE AD trial with patients who were ineligible. As the prescription of dabigatran has become increasingly popular, the generalizability of the results to real-world populations has come into question due to the broad variability of real-world patients receiving dabigatran. Our study aimed to identify all patients who were prescribed idarucizumab and examined how effectiveness and safety varied among those patients who were eligible and ineligible for the trial. Materials and Methods: This retrospective cohort study analyzed the largest medical database in Taiwan. We enrolled all patients who were prescribed and received idarucizumab from when it became available in Taiwan up until May 2021. A Total of 32 patients were included and analyzed, and they were further divided into subgroups based on their eligibility for inclusion in the RE-VERSE AD trial. Multiple outcomes were evaluated, including successful hemostasis rate, complete reversal efficacy of idarucizumab, 90-day thromboembolic events, intra-hospital mortality, and adverse event rate. Results: In our study, we found that 34.4% of real-world cases of idarucizumab use were ineligible for the RE-VERSE AD trials. The eligible group had higher successful hemostasis rates (95.2% vs. 80%) and anticoagulant effect reversal rates compared to the ineligible group (73.3% vs. 0%). The mortality rates were 9.5%, compared to 27.3% in the ineligible group. Few adverse effects (n = 3) and 90-day thromboembolic events (n = 1) were observed in either group. Among the ineligible cases, all acute ischemic stroke patients (n = 5) received definite, timely treatments without complications. Conclusions: Our study demonstrated the real-world effectiveness and safety of idarucizumab infusion for trial-eligible patients and all acute ischemic stroke patients. However, although it seems to be effective and safe, idarucizumab appears to be less effective in other trial-ineligible patients. Despite this result, our study provides further evidence for extending the applicability of idarucizumab in real-world scenarios. Our study suggests that idarucizumab can be a safe and effective option for reversing the anticoagulant effect of dabigatran, particularly for eligible patients.

A Step Forward in the Fight Against Dementia—Are We There Yet?

This Viewpoint reports on the results of the Clarity AD trial, a phase 3 randomized clinical trial of lecanemab for patients with early Alzheimer disease, in which lecanemab’s clinical efficacy was demonstrated using well-established outcome measures.

Lecanemab and APOE Genotyping in Clinical Practice—Navigating Uncharted Terrain

This Viewpoint discusses the findings of the Clarity AD trial, which studied lecanemab for patients with early-stage Alzheimer disease.

Evaluation of Blood-Based Plasma Biomarkers as Potential Markers of Amyloid Burden in Preclinical Alzheimer's Disease.

Participant eligibility for the A4 Study was determined by amyloid PET imaging. Given the disadvantages of amyloid PET imaging in accessibility and cost, blood-based biomarkers may serve as a sufficient biomarker and more cost-effective screening tool for patient enrollment into preclinical AD trials. To determine if a blood-based screening test can adequately identify amyloid burden in participants screened into a preclinical AD trial. In this cross-sectional study, 224 participants from the A4 Study received an amyloid PET scan (18Florbetapir) within 90 days of blood sample collection. Blood samples from all study participants were processed within 2 h after phlebotomy. Plasma amyloid measures were quantified by Shimazdu and C2 N Diagnostics using mass spectrometry-based platforms. A corresponding subset of blood samples (n = 100) was processed within 24 h after phlebotomy and analyzed by C2 N. Plasma Aβ42/Aβ40 demonstrated the highest association for Aβ accumulation in the brain with an AUC 0.76 (95%CI = 0.69, 0.82) at C2 N and 0.80 (95%CI = 0.75, 0.86) at Shimadzu. Blood samples processed to plasma within 2 h after phlebotomy provided a better prediction of amyloid PET status than blood samples processed within 24 h (AUC 0.80 versus 0.64; p < 0.001). Age, sex, and APOE ɛ4 carrier status did not the diagnostic performance of plasma Aβ42/Aβ40 to predict amyloid PET positivity in A4 Study participants. Plasma Aβ42/Aβ40 may serve as a potential biomarker for predicting elevated amyloid in the brain. Utilizing blood testing over PET imaging may improve screening efficiency into clinical trials.