Citations

Abstract INTRODUCTION Combining Alzheimer's disease blood biomarkers may improve prediction of amyloid positivity (A+) compared with single biomarkers. METHODS Logistic regression models using plasma phosphorylated tau 217 (p‐tau217), amyloid beta (Aβ) 42, and Aβ40 were developed in cognitively impaired (CI) participants from Mayo Clinic Rochester (MCR) and validated in Alzheimer's Disease Neuroimaging Initiative (ADNI) and Mayo Clinic Florida (MCF) cohorts. Performance was measured by area under the curve (AUC) and Probability of Amyloid Positivity Score (PAPS). RESULTS Plasma p‐tau217 alone showed high AUC (95% confidence intervals) across cohorts MCR, 0.928 (0.900–0.953); ADNI, 0.938 (0.915–0.959); MCF, 0.935 (0.893–0.970). Adding Aβ42 to regression models yielded marginal model improvement MCR, 0.940 (0.915–0.963); ADNI, 0.945 (0.922–0.965); MCF, 0.960 (0.925–0.986). Utilization of two cutpoints (≥ 90% sensitivity and specificity), yielded ≤ 10% intermediate results for p‐tau217, and ≤ 8% for p‐tau217+Aβ42. Addition of Aβ40 did not improve models’ performance. DISCUSSION An immunoassay‐based p‐tau217 PAPS model accurately predicted amyloid positivity across independent CI cohorts and achieved confirmatory‐test performance with two cutpoints. The added value of Aβ42 was modest and cohort dependent, supporting the potential of use of p‐tau217–based PAPS in clinical workflows.

ABSTRACT Retrospective cohort study to investigate which factors most reliably distinguish incidental GAD65‐antibodies (iGAD65) from true GAD65‐antibody neurological disease (tGAD65). 167 adults with positive serum and CSF GAD65‐antibodies (> 0.02 nmol/L by radioimmunoassay) were classified as tGAD65 or iGAD65, with diagnostic performance of paraclinical parameters compared in discovery and validation cohorts. CSF GAD65‐antibody titers ≥ 1 nmol/L performed best in discovery (AUC 0.956) and validation (AUC 0.949; p = 0.814), with 89.7% sensitivity and 94% specificity for identifying tGAD65. Serum titers > 20 nmol/L and indices of intrathecal synthesis showed lower discriminative ability. Our finding aims to reduce misdiagnosis and identify potential immunotherapy‐responsive diseases.

Abstract INTRODUCTION Cognitive characterization is increasingly important for Alzheimer’s disease treatment decisions, highlighting the need for scalable cognitive assessment. We evaluated the diagnostic accuracy of a remote self-administered cognitive screening battery, Mayo Test Drive (MTD), across clinically and biologically defined groups. METHODS In this prospectively designed study, 2,131 cognitively unimpaired (CU) and 118 cognitively impaired participants, including 96 with mild cognitive impairment and 22 with mild dementia (MCI/DEM), were recruited from observational cohort studies and completed MTD remotely (mean age=70 years, SD=12; 48% male; mean education=16 years, SD=2). Clinical diagnoses were determined by multidisciplinary consensus independently of MTD. Amyloid- and tau-PET were available for 1,224 participants. Logistic regression estimated associations between MTD and CU versus MCI/DEM, CU A-versus MCI/DEM A+, and A-T-versus A+T+. MTD diagnostic accuracy was compared with person-administered cognitive measures. Sensitivity and specificity across score types and comparison groups were examined. RESULTS MTD showed excellent discrimination of CU versus MCI/DEM (AUC=.89; 95% CI=.87-.92), and discriminability remained high for CU versus MCI (AUC=.88; 95% CI=.85-.92). Discriminability was highest when comparing CU A- and MCI/DEM A+ groups (AUC=.95; 95% CI=.92-.97). MTD outperformed estimated MMSE (p=.02) and was comparable to Mayo-PACC (p=.71) for A-T-versus A+T+ discrimination (AUC=.76; 95% CI=.71-.81). Combining both raw score and normative score cutoffs helped balance sensitivity and specificity relative to use of either cutoff in isolation. A two-stage interpretation framework further improved sensitivity while maintaining clinically useful specificity and informed development of an interpretation guide. DISCUSSION A remote self-administered digital cognitive assessment showed strong diagnostic accuracy for detecting MCI and mild dementia in a large, primarily population-based cohort. The remote, self-administered MTD composite also showed comparable performance to established in-person cognitive composites for differentiating amyloid and tau-defined biomarker groups. Remote self-administered digital cognitive assessment may complement existing cognitive evaluation approaches and support scalable cognitive characterization. Highlights Remote self-administered cognitive assessment showed strong differentiation of cognitively unimpaired and cognitively impaired participants. Diagnostic accuracy improved when amyloid PET biomarker status was incorporated. Remote assessment demonstrated sensitivity to amyloid and tau PET-defined Alzheimer’s disease. Performance was comparable to established in-person composites and better than an in-person mental status screening measure. Digital cognitive screening may support scalable treatment and clinical trial workflows.

Introduction: Frontotemporal lobar degeneration (FTLD) is a devastating disease that commonly results in early onset dementia, yet its rarity and heterogeneity limit large-scale research and clinical trials. Remote monitoring via low-burden digital health tools may overcome these barriers. We evaluated clinical utility of passive smartphone monitoring in FTLD using a multi-domain mobile assessment platform. Methods: Participants were 567 adults (53% clinically normal, 21% prodromal FTLD, 26% symptomatic FTLD) who completed an in-person study visit and downloaded the ALLFTD Mobile App on their personal smartphones. The app delivered unsupervised cognitive tests and passively collected continuous data on battery percentage (proxy for smartphone use) and step count. Longitudinal follow-up included smartphone monitoring and annual in-person study visits. Primary analyses examined associations between passive smartphone features and markers of disease severity at baseline and longitudinally, and tested whether passive features added incremental value beyond app-based cognitive testing. Results: Passive smartphone features were feasible to collect and showed excellent reliability with <2 weeks of monitoring (ICCs>0.9). Features capturing smartphone use and movement demonstrated sensitivity to gold-standard clinical measures of disease severity both at baseline and longitudinally. A classification model combining passive features, the app-based cognitive composite score, and demographics detected longitudinal functional decline (AUC=0.89); passive features alone (AUC=0.84) performed comparably to a cognitive screener (AUC=0.85). Discussion: Passive smartphone monitoring is a valid, zero-burden measure of disease severity and progression in FTLD that adds modest incremental value beyond app-based cognitive testing. Findings support its use as a scalable digital endpoint in longitudinal FTLD research.

Abstract INTRODUCTION Plasma p‐tau217 and p‐tau217/Aβ42 reliably identify non‐Hispanic White individuals with symptomatic and pre‐clinical AD. However, performance across ethnoracially diverse groups remains unknown. METHODS Cognitively normal Black, Hispanic, and non‐Hispanic White participants completed cognitive evaluations, brain magnetic resonance imaging (MRI), amyloid and tau positron emission tomography (PET), and measures of kidney function. Plasma p‐tau217 and Aβ42 were measured using Fujirebio Lumipulse assays. Pre‐clinical AD detection was evaluated using symptomatic and cohort‐derived optimized cutoffs. RESULTS Among 191 participants (57 Black, 46 Hispanic White, 88 non‐Hispanic White), age‐adjusted plasma p‐tau217 and p‐tau217/Aβ42 performed similarly in detecting amyloid‐positive individuals (≥ 25 Centiloids), regardless of race/ethnicity. Both measures independently correlated with amyloid deposition, whereas estimated glomerular filtration rate (eGFR) correlated solely with p‐tau217. Pre‐clinical AD optimized cutoffs (p‐tau217 ≥ 0.132 pg/ml; p‐tau217/Aβ42 ≥ 0.0059) outperformed cutoffs established in symptomatic individuals (sensitivity: 83% vs. 57%; 77% vs. 63%; negative predictive value [NPV] > 93%). DISCUSSION Plasma biomarkers performed similarly across ethnoracially diverse cohorts. Optimized cutoffs may improve early detection and trial enrollment.