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INTRODUCTION: We evaluated preliminary feasibility of a digital, culturally-informed approach to recruit and screen participants for the Alzheimers Disease Neuroimaging Initiative (ADNI4). METHODS: Participants were recruited using digital advertising and completed digital surveys (e.g., demographics, medical exclusion criteria, 12-item Everyday Cognition Scale [ECog-12]), Novoic Storyteller speech-based cognitive test). Completion rates and assessment performance were compared between underrepresented populations (URPs: individuals from ethnoculturally minoritized or low education backgrounds) and non-URPs. RESULTS: Of 3099 participants who provided contact information, 654 enrolled in the cohort, and 595 completed at least one assessment. Two hundred forty-seven participants were from URPs. Of those enrolled, 465 met ADNI4 inclusion criteria and 237 evidenced possible cognitive impairment from ECog-12 or Storyteller performance. URPs had lower ECog and Storyteller completion rates. Scores varied by ethnocultural group and educational level. DISCUSSION: Preliminary results demonstrate digital recruitment and screening assessment of an older diverse cohort, including those with possible cognitive impairment, are feasible. Improving engagement and achieving educational diversity are key challenges. HIGHLIGHTS: A total of 654 participants enrolled in a digital cohort to facilitate ADNI4 recruitment. Culturally-informed digital ads aided enrollment of underrepresented populations. From those enrolled, 42% were from underrepresented ethnocultural and educational groups. Digital screening tools indicate > 50% of participants likely cognitively impaired. Completion rates and assessment performance vary by ethnocultural group and education.

INTRODUCTION: In trials of amyloid-lowering drugs for Alzheimers disease (AD), differential eligibility may contribute to under-inclusion of racial and ethnic underrepresented groups. We examined plasma amyloid beta 42/40 and positron emission tomography (PET) amyloid eligibility for the ongoing AHEAD Study preclinical AD program (NCT04468659). METHODS: Univariate logistic regression models were used to examine group differences in plasma and PET amyloid screening eligibility. RESULTS: Of 4905 participants screened at time of analysis, 1724 were plasma eligible to continue in screening: 13.3% Hispanic Black, 24.7% Hispanic White, 20.8% non-Hispanic (NH) Asian, 24.7% NH Black, and 38.9% NH White. Plasma eligibility differed across groups in models controlling for covariates (odds ratio from 1.9 to 4.0 compared to the NH White reference group, P

BackgroundPrior studies of Alzheimer's disease (AD) biomarker disclosure have answered important questions about individuals' safety after learning and comprehending their amyloid PET results; however, these studies have typically employed highly structured disclosure protocols and focused on the psychological impact of disclosure (e.g., anxiety, depression, and suicidality) in homogeneous populations. More work is needed to develop flexible disclosure protocols and study outcomes in ethnoculturally representative samples.MethodsThe Alzheimer's Disease Neuroimaging Initiative (ADNI) is formally incorporating amyloid PET disclosure into the newest protocol (ADNI-4). Participants across the cognitive spectrum who wish to know their amyloid PET results may learn them. The pragmatic disclosure process spans four timepoints: (1) a pre-disclosure visit, (2) the PET scan and its read, (3) a disclosure visit, and (4) a post-disclosure check-in. This process applies to all participants, with slight modifications to account for their cognitive status. In designing this process, special emphasis was placed on utilizing investigator discretion. Participant measures include perceived risk of dementia, purpose in life, and disclosure satisfaction. Investigator assessment of the disclosure visit (e.g., challenges encountered, topics discussed, etc.) is also included.ResultsData collection is ongoing. Results will allow for more robust characterization of the impact of learning amyloid PET results on individuals and describe the perspectives of investigators.ConclusionThe pragmatic design of the disclosure process in ADNI-4 coupled with the novel participant and investigator data will inform future disclosure practices. This is especially important as disclosure of biomarker results expands in research and care.

Abstract Determining the genetic architecture of Alzheimer’s disease pathologies can enhance mechanistic understanding and inform precision medicine strategies. Here, we perform a genome-wide association study of cortical tau quantified by positron emission tomography in 3046 participants from 12 independent studies. The CYP1B1-RMDN2 locus is associated with tau deposition. The most significant signal is at rs2113389, explaining 4.3% of the variation in cortical tau, while APOE4 rs429358 accounts for 3.6%. rs2113389 is associated with higher tau and faster cognitive decline. Additive effects, but no interactions, are observed between rs2113389 and diagnosis, APOE4, and amyloid beta positivity. CYP1B1 expression is upregulated in AD. rs2113389 is associated with higher CYP1B1 expression and methylation levels. Mouse model studies provide additional functional evidence for a relationship between CYP1B1 and tau deposition but not amyloid beta. These results provide insight into the genetic basis of cerebral tau deposition and support novel pathways for therapeutic development in AD.

IntroductionOne goal of the Longitudinal Early Onset Alzheimer's Disease Study (LEADS) is to define the fluid biomarker characteristics of early-onset Alzheimer's disease (EOAD).MethodsCerebrospinal fluid (CSF) concentrations of Aβ1-40, Aβ1-42, total tau (tTau), pTau181, VILIP-1, SNAP-25, neurogranin (Ng), neurofilament light chain (NfL), and YKL-40 were measured by immunoassay in 165 LEADS participants. The associations of biomarker concentrations with diagnostic group and standard cognitive tests were evaluated.ResultsBiomarkers were correlated with one another. Levels of CSF Aβ42/40, pTau181, tTau, SNAP-25, and Ng in EOAD differed significantly from cognitively normal and early-onset non-AD dementia; NfL, YKL-40, and VILIP-1 did not. Across groups, all biomarkers except SNAP-25 were correlated with cognition. Within the EOAD group, Aβ42/40, NfL, Ng, and SNAP-25 were correlated with at least one cognitive measure.DiscussionThis study provides a comprehensive analysis of CSF biomarkers in sporadic EOAD that can inform EOAD clinical trial design.

ObjectiveInvestigation of learning slopes in early-onset dementias has been limited. The current study aimed to highlight the sensitivity of learning slopes to discriminate disease severity in cognitively normal participants and those diagnosed with early-onset dementia with and without β-amyloid positivity METHOD: Data from 310 participants in the Longitudinal Early-Onset Alzheimer's Disease Study (aged 41 to 65) were used to calculate learning slope metrics. Learning slopes among diagnostic groups were compared, and the relationships of slopes with standard memory measures were determined RESULTS: Worse learning slopes were associated with more severe disease states, even after controlling for demographics, total learning, and cognitive severity. A particular metric-the learning ratio (LR)-outperformed other learning slope calculations across analyses CONCLUSIONS: Learning slopes appear to be sensitive to early-onset dementias, even when controlling for the effect of total learning and cognitive severity. The LR may be the learning measure of choice for such analyses.HighlightsLearning is impaired in amyloid-positive EOAD, beyond cognitive severity scores alone. Amyloid-positive EOAD participants perform worse on learning slopes than amyloid-negative participants. Learning ratio appears to be the learning metric of choice for EOAD participants.

IntroductionWe examined neuropsychiatric symptoms (NPS) and psychotropic medication use in a large sample of individuals with early-onset Alzheimer's disease (EOAD; onset 40-64 years) at the midway point of data collection for the Longitudinal Early-onset Alzheimer's Disease Study (LEADS).MethodsBaseline NPS (Neuropsychiatric Inventory - Questionnaire; Geriatric Depression Scale) and psychotropic medication use from 282 participants enrolled in LEADS were compared across diagnostic groups - amyloid-positive EOAD (n = 212) and amyloid negative early-onset non-Alzheimer's disease (EOnonAD; n = 70).ResultsAffective behaviors were the most common NPS in EOAD at similar frequencies to EOnonAD. Tension and impulse control behaviors were more common in EOnonAD. A minority of participants were using psychotropic medications, and use was higher in EOnonAD.DiscussionOverall NPS burden and psychotropic medication use were higher in EOnonAD than EOAD participants. Future research will investigate moderators and etiological drivers of NPS, and NPS differences in EOAD versus late-onset AD.

ObjectiveThe Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) seeks to provide comprehensive understanding of early-onset Alzheimer's disease (EOAD; onset

IntroductionOne goal of the Longitudinal Early-onset Alzheimer's Disease Study (LEADS) is to investigate the genetic etiology of early onset (40-64 years) cognitive impairment. Toward this goal, LEADS participants are screened for known pathogenic variants.MethodsLEADS amyloid-positive early-onset Alzheimer's disease (EOAD) or negative early-onset non-AD (EOnonAD) cases were whole exome sequenced (N = 299). Pathogenic variant frequency in APP, PSEN1, PSEN2, GRN, MAPT, and C9ORF72 was assessed for EOAD and EOnonAD. Gene burden testing was performed in cases compared to similar-age cognitively normal controls in the Parkinson's Progression Markers Initiative (PPMI) study.ResultsPreviously reported pathogenic variants in the six genes were identified in 1.35% of EOAD (3/223) and 6.58% of EOnonAD (5/76). No genes showed enrichment for carriers of rare functional variants in LEADS cases.DiscussionResults suggest that LEADS is enriched for novel genetic causative variants, as previously reported variants are not observed in most cases.HighlightsSequencing identified eight cognitively impaired pathogenic variant carriers. Pathogenic variants were identified in PSEN1, GRN, MAPT, and C9ORF72. Rare variants were not enriched in APP, PSEN1/2, GRN, and MAPT. The Longitudinal Early-onset Alzheimer's Disease Study (LEADS) is a key resource for early-onset Alzheimer's genetic research.

IntroductionWe compared white matter hyperintensities (WMHs) in early-onset Alzheimer's disease (EOAD) with cognitively normal (CN) and early-onset amyloid-negative cognitively impaired (EOnonAD) groups in the Longitudinal Early-Onset Alzheimer's Disease Study.MethodsWe investigated the role of increased WMH in cognition and amyloid and tau burden. We compared WMH burden of 205 EOAD, 68 EOnonAD, and 89 CN participants in lobar regions using t-tests and analyses of covariance. Linear regression analyses were used to investigate the association between WMH and cognitive impairment and that between amyloid and tau burden.ResultsEOAD showed greater WMHs compared with CN and EOnonAD participants across all regions with no significant differences between CN and EOnonAD groups. Greater WMHs were associated with worse cognition. Tau burden was positively associated with WMH burden in the EOAD group.DiscussionEOAD consistently showed higher WMH volumes. Overall, greater WMHs were associated with worse cognition and higher tau burden in EOAD.HighlightsThis study represents a comprehensive characterization of WMHs in sporadic EOAD. WMH volumes are associated with tau burden from positron emission tomography (PET) in EOAD, suggesting WMHs are correlated with increasing burden of AD. Greater WMH volumes are associated with worse performance on global cognitive tests. EOAD participants have higher WMH volumes compared with CN and early-onset amyloid-negative cognitively impaired (EOnonAD) groups across all brain regions.

BackgroundDeep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics.MethodsThe c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification.FindingsThe application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6).InterpretationOur results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation.FundingThe specific funding of this article is provided in the acknowledgements section.

Mechanisms of resilience against tau pathology in individuals across the Alzheimer's disease spectrum are insufficiently understood. Longitudinal data are necessary to reveal which factors relate to preserved cognition (i.e. cognitive resilience) and brain structure (i.e. brain resilience) despite abundant tau pathology, and to clarify whether these associations are cross-sectional or longitudinal. We used a longitudinal study design to investigate the role of several demographic, biological and brain structural factors in yielding cognitive and brain resilience to tau pathology as measured with PET. In this multicentre study, we included 366 amyloid-β-positive individuals with mild cognitive impairment or Alzheimer's disease dementia with baseline 18F-flortaucipir-PET and longitudinal cognitive assessments. A subset (n = 200) additionally underwent longitudinal structural MRI. We used linear mixed-effects models with global cognition and cortical thickness as dependent variables to investigate determinants of cognitive resilience and brain resilience, respectively. Models assessed whether age, sex, years of education, APOE-ε4 status, intracranial volume (and cortical thickness for cognitive resilience models) modified the association of tau pathology with cognitive decline or cortical thinning. We found that the association between higher baseline tau-PET levels (quantified in a temporal meta-region of interest) and rate of cognitive decline (measured with repeated Mini-Mental State Examination) was adversely modified by older age (Stβinteraction = -0.062, P = 0.032), higher education level (Stβinteraction = -0.072, P = 0.011) and higher intracranial volume (Stβinteraction = -0.07, P = 0.016). Younger age, higher education and greater cortical thickness were associated with better cognitive performance at baseline. Greater cortical thickness was furthermore associated with slower cognitive decline independent of tau burden. Higher education also modified the negative impact of tau-PET on cortical thinning, while older age was associated with higher baseline cortical thickness and slower rate of cortical thinning independent of tau. Our analyses revealed no (cross-sectional or longitudinal) associations for sex and APOE-ε4 status on cognition and cortical thickness. In this longitudinal study of clinically impaired individuals with underlying Alzheimer's disease neuropathological changes, we identified education as the most robust determinant of both cognitive and brain resilience against tau pathology. The observed interaction with tau burden on cognitive decline suggests that education may be protective against cognitive decline and brain atrophy at lower levels of tau pathology, with a potential depletion of resilience resources with advancing pathology. Finally, we did not find major contributions of sex to brain nor cognitive resilience, suggesting that previous links between sex and resilience might be mainly driven by cross-sectional differences.

Abstract Mitochondrial forms account approximately 1–2% of all nonsyndromic cases of hearing loss (HL). One of the most common causative variants of mtDNA is the m.1555A > G variant of the MT-RNR1 gene (OMIM 561000). Currently the detection of the m.1555A > G variant of the MT-RNR1 gene is not included in all research protocols. In this study this variant was screened among 165 patients with HL from the Republic of Buryatia, located in the Baikal Lake region of Russia. In our study, the total contribution of the m.1555A > G variant to the etiology of HL was 12.7% (21/165), while the update global prevalence of this variant is 1.8% (863/47,328). The m.1555A > G variant was notably more prevalent in Buryat (20.2%) than in Russian patients (1.3%). Mitogenome analysis in 14 unrelated Buryat families carrying the m.1555A > G variant revealed a predominant lineage: in 13 families, a cluster affiliated with sub-haplogroup A5b (92.9%) was identified, while one family had the D5a2a1 lineage (7.1%). In a Russian family with the m.1555A > G variant the lineage affiliated with sub-haplogroup F1a1d was found. Considering that more than 90% of Buryat families with the m.1555A > G variant belong to the single maternal lineage cluster we conclude that high prevalence of this variant in patients with HL in the Baikal Lake region can be attributed to a founder effect.

Zero-dimensional heterometallic halide hybrids have emerged as multifunctional emitters, due to their multiple emission features. Herein, high pressure is employed to regulate the multiple emissions of (C9NH20)9[Pb3Br11](MnBr4)2. Pressure-triggered rigidness of [Pb3Br11]5- alters the emission equilibrium of STEs, generating a new blue-shifted STE emission. Meanwhile, [MnBr4]2- contraction promotes the confinement effect to increase the 4D-6A1 transition efficiency. Notably, [Pb3Br11]5- could act as ‘antenna' to enhance the Mn2+ emission with blocked back-energy transfer. It indicates that enough incident energy could excite the STE emission and 4D-6A1 transitions simultaneously, which clarifies the conventional understanding of the emission origin at ambient conditions.

The poor generalizability of clinical research data due to the enrollment of highly educated, non-Latinx White participants hampers the development of therapies for Alzheimers disease (AD). Black and Latinx older adults have a greater risk for dementia, yet it is unclear how health-care disparities and sociocultural factors influence potential AD therapies and prognosis. Low enrollment of under-represented populations may be attributable to several factors including greater exclusion due to higher rates of comorbidities, lower access to AD clinics, and the legacy of unethical treatment in medical research. This perspective outlines solutions tested in the Brain Health Registry (BHR) and the Alzheimers Disease Neuroimaging Initiative (ADNI), including culturally-informed digital research methods, community-engaged research strategies, leadership from under-represented communities, and the reduction of exclusion criteria based on comorbidities. Our successes demonstrate that it is possible to increase the inclusion and engagement of under-represented populations into US-based clinical studies, thereby increasing the generalizability of their results.

INTRODUCTION: The Alzheimers disease (AD) continuum begins with a long asymptomatic or preclinical stage, during which amyloid beta (Aβ) is accumulating for more than a decade prior to widespread cortical tauopathy, neurodegeneration, and manifestation of clinical symptoms. The AHEAD 3-45 Study (BAN2401-G000-303) is testing whether intervention with lecanemab (BAN2401), a humanized immunoglobulin 1 (IgG1) monoclonal antibody that preferentially targets soluble aggregated Aβ, initiated during this asymptomatic stage can slow biomarker changes and/or cognitive decline. The AHEAD 3-45 Study is conducted as a Public-Private Partnership of the Alzheimers Clinical Trial Consortium (ACTC), funded by the National Institute on Aging, National Institutes of Health (NIH), and Eisai Inc. METHODS: The AHEAD 3-45 Study was launched on July 14, 2020, and consists of two sister trials (A3 and A45) in cognitively unimpaired (CU) individuals ages 55 to 80 with specific dosing regimens tailored to baseline brain amyloid levels on screening positron emission tomography (PET) scans: intermediate amyloid (≈20 to 40 Centiloids) for A3 and elevated amyloid (>40 Centiloids) for A45. Both trials are being conducted under a single protocol, with a shared screening process and common schedule of assessments. A3 is a Phase 2 trial with PET-imaging end points, whereas A45 is a Phase 3 trial with a cognitive composite primary end point. The treatment period is 4 years. The study utilizes innovative approaches to enriching the sample with individuals who have elevated brain amyloid. These include recruiting from the Trial-Ready Cohort for Preclinical and Prodromal Alzheimers disease (TRC-PAD), the Australian Dementia Network (ADNeT) Registry, and the Japanese Trial Ready Cohort (J-TRC), as well as incorporation of plasma screening with the C2N mass spectrometry platform to quantitate the Aβ 42/40 ratio (Aβ 42/40), which has been shown previously to reliably identify cognitively normal participants not likely to have elevated brain amyloid levels. A blood sample collected at a brief first visit is utilized to screen out individuals who are less likely to have elevated brain amyloid, and to determine the participants eligibility to proceed to PET imaging. Eligibility to randomize into the A3 Trial or A45 Trial is based on the screening PET imaging results. RESULT: The focus of this article is on the innovative design of the study. DISCUSSION: The AHEAD 3-45 Study will test whether with lecanemab (BAN2401) can slow the accumulation of tau and prevent the cognitive decline associated with AD during its preclinical stage. It is specifically targeting both the preclinical and the early preclinical (intermediate amyloid) stages of AD and is the first secondary prevention trial to employ plasma-based biomarkers to accelerate the screening process and potentially substantially reduce the number of screening PET scans.

Clinical trials for Alzheimers disease (AD) are slower to enroll study participants, take longer to complete, and are more expensive than trials in most other therapeutic areas. The recruitment and retention of a large number of qualified, diverse volunteers to participate in clinical research studies remain among the key barriers to the successful completion of AD clinical trials. An advisory panel of experts from academia, patient-advocacy organizations, philanthropy, non-profit, government, and industry convened in 2020 to assess the critical challenges facing recruitment in Alzheimers clinical trials and develop a set of recommendations to overcome them. This paper briefly reviews existing challenges in AD clinical research and discusses the feasibility and implications of the panels recommendations for actionable and inclusive solutions to accelerate the development of novel therapies for AD.

BackgroundParticipant 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.ObjectiveTo determine if a blood-based screening test can adequately identify amyloid burden in participants screened into a preclinical AD trial.MethodsIn 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.ResultsPlasma 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

Lecanemab (Leqembi®) is approved in the United States for the treatment of Alzheimer's disease (AD) to be initiated in early AD (mild cognitive impairment [MCI] due to AD or mild AD dementia) with confirmed brain amyloid pathology. Appropriate Use Recommendations (AURs) are intended to help guide the introduction of new therapies into real-world clinical practice. Community dwelling patients with AD differ from those participating in clinical trials. Administration of lecanemab at clinical trial sites by individuals experienced with monoclonal antibody therapy also differs from the community clinic-based administration of lecanemab. These AURs use clinical trial data as well as research and care information regarding AD to help clinicians administer lecanemab with optimal safety and opportunity for effectiveness. Safety and efficacy of lecanemab are known only for patients like those participating in the phase 2 and phase 3 lecanemab trials, and these AURs adhere closely to the inclusion and exclusion criteria of the trials. Adverse events may occur with lecanemab including amyloid related imaging abnormalities (ARIA) and infusion reactions. Monitoring guidelines for these events are detailed in this AUR. Most ARIA with lecanemab is asymptomatic, but a few cases are serious or, very rarely, fatal. Microhemorrhages and rare macrohemorrhages may occur in patients receiving lecanemab. Anticoagulation increases the risk of hemorrhage, and the AUR recommends that patients requiring anticoagulants not receive lecanemab until more data regarding this interaction are available. Patients who are apolipoprotein E ε4 (APOE4) gene carriers, especially APOE4 homozygotes, are at higher risk for ARIA, and the AUR recommends APOE genotyping to better inform risk discussions with patients who are lecanemab candidates. Clinician and institutional preparedness are mandatory for use of lecanemab, and protocols for management of serious events should be developed and implemented. Communication between clinicians and therapy candidates or those on therapy is a key element of good clinical practice for the use of lecanemab. Patients and their care partners must understand the potential benefits, the potential harms, and the monitoring requirements for treatment with this agent. Culture-specific communication and building of trust between clinicians and patients are the foundation for successful use of lecanemab.

BackgroundRecruiting to multi-site trials is challenging, particularly when striving to ensure the randomized sample is demographically representative of the larger disease-suffering population. While previous studies have reported disparities by race and ethnicity in enrollment and randomization, they have not typically investigated whether disparities exist in the recruitment process prior to consent. To identify participants most likely to be eligible for a trial, study sites frequently include a prescreening process, generally conducted by telephone, to conserve resources. Collection and analysis of such prescreening data across sites could provide valuable information to improve understanding of recruitment intervention effectiveness, including whether traditionally underrepresented participants are lost prior to screening.MethodsWe developed an infrastructure within the National Institute on Aging (NIA) Alzheimer's Clinical Trials Consortium (ACTC) to centrally collect a subset of prescreening variables. Prior to study-wide implementation in the AHEAD 3-45 study (NCT NCT04468659), an ongoing ACTC trial recruiting older cognitively unimpaired participants, we completed a vanguard phase with seven study sites. Variables collected included age, self-reported sex, self-reported race, self-reported ethnicity, self-reported education, self-reported occupation, zip code, recruitment source, prescreening eligibility status, reason for prescreen ineligibility, and the AHEAD 3-45 participant ID for those who continued to an in-person screening visit after study enrollment.ResultsEach of the sites was able to submit prescreening data. Vanguard sites provided prescreening data on a total of 1029 participants. The total number of prescreened participants varied widely among sites (range 3-611), with the differences driven mainly by the time to receive site approval for the main study. Key learnings instructed design/informatic/procedural changes prior to study-wide launch.ConclusionCentralized capture of prescreening data in multi-site clinical trials is feasible. Identifying and quantifying the impact of central and site recruitment activities, prior to participants signing consent, has the potential to identify and address selection bias, instruct resource use, contribute to effective trial design, and accelerate trial enrollment timelines.

IntroductionThe Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to validate biomarkers for Alzheimer's disease (AD) clinical trials. To improve generalizability, ADNI4 aims to enroll 50-60% of its new participants from underrepresented populations (URPs) using new biofluid and digital technologies. ADNI4 has received funding from the National Institute on Aging beginning September 2022.MethodsADNI4 will recruit URPs using community-engaged approaches. An online portal will screen 20,000 participants, 4000 of whom (50-60% URPs) will be tested for plasma biomarkers and APOE. From this, 500 new participants will undergo in-clinic assessment joining 500 ADNI3 rollover participants. Remaining participants (∼3500) will undergo longitudinal plasma and digital cognitive testing. ADNI4 will add MRI sequences and new PET tracers. Project 1 will optimize biomarkers in AD clinical trials.Results and discussionADNI4 will improve generalizability of results, use remote digital and blood screening, and continue providing longitudinal clinical, biomarker, and autopsy data to investigators.

Apathy is one of the most common neuropsychiatric symptoms (NPS) and is associated with poor clinical outcomes. Research that helps define the apathy phenotype is urgently needed, particularly for clinical and biomarker studies. We used latent class analysis (LCA) with two independent cohorts to understand how apathy and depression symptoms co-occur statistically. We further explored the relationship between latent class membership, demographics, and the presence of other NPS. The LCA identified a four-class solution (no symptoms, apathy, depression, and combined apathy/depression), reproducible over both cohorts, providing robust support for an apathy syndrome distinct from depression and confirming that an apathy/depression syndrome exists, supported by the model fit test with the four-class solution scores evidencing better fitting (Bayesian information criterion adjusted and entropy R 2). Using a data-driven method, we show distinct and statistically meaningful co-occurrence of apathy and depressive symptoms. There was evidence that these classes have different clinical associations, which may help inform diagnostic categories for research studies and clinical practice.HighlightsWe found four classes: no symptoms, apathy, depression and apathy/depression.Apathy conferred a higher probability for agitation.Apathy diagnostic criteria should include accompanying neuropsychiatric symptoms.

INTRODUCTION: An analysis of the ethnocultural and socioeconomic composition of Alzheimers Disease Neuroimaging Initiative (ADNI) participants is needed to assess the generalizability of ADNI data to diverse populations. METHODS: ADNI data collected between October 2004 and November 2020 were used to determine ethnocultural and educational composition of the sample and differences in the following metrics: screening, screen fails, enrollment, biomarkers. RESULTS: Of 3739 screened individuals, 11% identified as being from ethnoculturally underrepresented populations (e.g., Black, Latinx) and 16% had

Abstract Background and Aims This study examines the relationships between epicardial adipose tissue (EAT), nonalcoholic fatty liver disease (NAFLD), and coronary artery calcium score (CACS) using non‐ECG‐gated CT scans. It aims to determine the effectiveness of EAT measurements and NAFLD as predictors for coronary artery disease (CAD). Methods This cross‐sectional study was conducted at a specialized center, focusing on individuals who underwent non‐ECG‐gated chest CT scans without contrast. We evaluated EAT thickness and density in three areas: the right atrioventricular groove, the free wall of the right ventricle, and the central area of the right anterior interventricular groove. Additionally, we measured CACS and determined the presence of NAFLD by comparing liver‐to‐spleen density ratios. Statistical analyses, including regression models, were performed using SPSS (version 26). Results In this study, we enrolled 365 participants, including 203 males with an average age of 47 ± 17.93 years. EAT thickness was 6.28 ± 1.62 mm, and EAT density was −96.07 ± 12.47 Hounsfield units (HU). The mean CACS was 22.27 ± 79.01, and the mean liver density was 50.01 ± 10.76 HU. A significant positive correlation was observed between EAT thickness and CACS (r = 0.208, p

Summary: Background: Research in healthy young adults shows that characteristic patterns of brain activity define individual “brain-fingerprints” that are unique to each person. However, variability in these brain-fingerprints increases in individuals with neurological conditions, challenging the clinical relevance and potential impact of the approach. Our study shows that brain-fingerprints derived from neurophysiological brain activity are associated with pathophysiological and clinical traits of individual patients with Parkinson’s disease (PD). Methods: We created brain-fingerprints from task-free brain activity recorded through magnetoencephalography in 79 PD patients and compared them with those from two independent samples of age-matched healthy controls (N = 424 total). We decomposed brain activity into arrhythmic and rhythmic components, defining distinct brain-fingerprints for each type from recording durations of up to 4 min and as short as 30 s. Findings: The arrhythmic spectral components of cortical activity in patients with Parkinson’s disease are more variable over short periods, challenging the definition of a reliable brain-fingerprint. However, by isolating the rhythmic components of cortical activity, we derived brain-fingerprints that distinguished between patients and healthy controls with about 90% accuracy. The most prominent cortical features of the resulting Parkinson’s brain-fingerprint are mapped to polyrhythmic activity in unimodal sensorimotor regions. Leveraging these features, we also demonstrate that Parkinson’s symptom laterality can be decoded directly from cortical neurophysiological activity. Furthermore, our study reveals that the cortical topography of the Parkinson’s brain-fingerprint aligns with that of neurotransmitter systems affected by the disease’s pathophysiology. Interpretation: The increased moment-to-moment variability of arrhythmic brain-fingerprints challenges patient differentiation and explains previously published results. We outline patient-specific rhythmic brain signaling features that provide insights into both the neurophysiological signature and symptom laterality of Parkinson’s disease. Thus, the proposed definition of a rhythmic brain-fingerprint of Parkinson’s disease may contribute to novel, refined approaches to patient stratification. Symmetrically, we discuss how rhythmic brain-fingerprints may contribute to the improved identification and testing of therapeutic neurostimulation targets. Funding: Data collection and sharing for this project was provided by the Quebec Parkinson Network (QPN), the Pre-symptomatic Evaluation of Novel or Experimental Treatments for Alzheimer’s Disease (PREVENT-AD; release 6.0) program, the Cambridge Centre for Aging Neuroscience (Cam-CAN), and the Open MEG Archives (OMEGA). The QPN is funded by a grant from Fonds de Recherche du Québec - Santé (FRQS). PREVENT-AD was launched in 2011 as a $13.5 million, 7-year public-private partnership using funds provided by McGill University, the FRQS, an unrestricted research grant from Pfizer Canada, the Levesque Foundation, the Douglas Hospital Research Centre and Foundation, the Government of Canada, and the Canada Fund for Innovation. The Brainstorm project is supported by funding to SB from the NIH (R01-EB026299-05). Further funding to SB for this study included a Discovery grant from the Natural Sciences and Engineering Research Council of Canada of Canada (436355-13), and the CIHR Canada research Chair in Neural Dynamics of Brain Systems (CRC-2017-00311).

The extent to which the pathophysiology of autosomal dominant Alzheimer's disease corresponds to the pathophysiology of 'sporadic' late onset Alzheimer's disease is unknown, thus limiting the extrapolation of study findings and clinical trial results in autosomal dominant Alzheimer's disease to late onset Alzheimer's disease. We compared brain MRI and amyloid PET data, as well as CSF concentrations of amyloid-β42, amyloid-β40, tau and tau phosphorylated at position 181, in 292 carriers of pathogenic variants for Alzheimer's disease from the Dominantly Inherited Alzheimer Network, with corresponding data from 559 participants from the Alzheimer's Disease Neuroimaging Initiative. Imaging data and CSF samples were reprocessed as appropriate to guarantee uniform pipelines and assays. Data analyses yielded rates of change before and after symptomatic onset of Alzheimer's disease, allowing the alignment of the ∼30-year age difference between the cohorts on a clinically meaningful anchor point, namely the participant age at symptomatic onset. Biomarker profiles were similar for both autosomal dominant Alzheimer's disease and late onset Alzheimer's disease. Both groups demonstrated accelerated rates of decline in cognitive performance and in regional brain volume loss after symptomatic onset. Although amyloid burden accumulation as determined by PET was greater after symptomatic onset in autosomal dominant Alzheimer's disease than in late onset Alzheimer's disease participants, CSF assays of amyloid-β42, amyloid-β40, tau and p-tau181 were largely overlapping in both groups. Rates of change in cognitive performance and hippocampal volume loss after symptomatic onset were more aggressive for autosomal dominant Alzheimer's disease participants. These findings suggest a similar pathophysiology of autosomal dominant Alzheimer's disease and late onset Alzheimer's disease, supporting a shared pathobiological construct.

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu ) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer's Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.