The development of treatments for Alzheimer’s disease (AD), dementia with Lewy bodies (DLB), and Parkinson’s disease (PD) is hampered by the scarce understanding of disease-specific and shared pathophysiological mechanisms. Cohorts covering AD, DLB, and PD with multiple, harmonized in vivo biomarkers of both established pathology and broader molecular processes are scarce, limiting comparisons across diseases. Therefore, we set up the European Platform for Neurodegenerative Diseases (EPND) cohort. Here, we describe its design and methods. Additionally, we explore disease-specific and shared cerebrospinal fluid (CSF) proteomic changes across diseases. To build the EPND cohort, participants with AD, DLB, PD, or controls were selected from ten European cohorts. Samples were centrally analysed. Amyloid-β (Aβ) 42, Aβ40, phosphorylated tau (p-tau) 181, and neurofilament light were measured in CSF and plasma, and total α-synuclein in CSF (all using Elecsys). Complement assays were performed in CSF (Milliplex) and plasma (SVAR), and proteomics panels (Olink Explore 3072, SomaScan 7k) were measured in CSF. Cohort data on demographics, clinical information, cognition, rating scales, lifestyle, genetics, and key imaging measures were centrally harmonized. Using this cohort, we explored CSF proteins (n = 1617) uniquely and commonly dysregulated across diseases, measured by Olink proximity extension assays. Data were analyzed by linear regression adjusted for age, sex, and mean CSF protein level (average z-scored level of all highly detected Olink proteins). P-values were FDR-corrected. 353 participants were included (age 67.1 [SD 9.2], 133 [38
Identifying which patients with mild cognitive impairment (MCI) will progress rapidly to Alzheimer’s disease (AD) within 24 months has direct implications for anti-amyloid therapy candidacy, monitoring intensity, and prognosis. Current machine learning approaches report high area under the curve (AUC) values for any-conversion prediction but conflate slow and rapid progressors, limiting clinical utility. A fundamentally different approach is needed: one that targets the rapid-conversion subphenotype with interpretable, routine biomarker inputs. We applied spectral decomposition to a 10 × 10 Pearson correlation matrix of 10 routine multimodal biomarkers (cognitive, functional, structural MRI, and CSF) from 607 MCI participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Eigendecomposition identified two disease-sensitive eigenmodes: E7 (cognitive-functional instability) and E9 (structural-pathological burden). Subject-level E7 and E9 scores were derived by projecting individual biomarker profiles onto MCI-derived eigenvectors and served as inputs to logistic regression. Performance was evaluated on a stratified hold-out test set ( n = 122 ) and validated by 5-fold cross-validation. Models were benchmarked against raw biomarker and PCA-based comparators using DeLong’s method with Bonferroni correction. E79 achieved AUC 0.891 (bootstrap 95
Real-world safety data for donanemab in China remain limited, particularly during early treatment and progressive dose titration. We analyzed 108 participants with symptomatic AD treated with donanemab at six Chinese centers using a 4-weekly progressive titration regimen. Safety assessments included infusion-related reactions (IRRs), amyloid-related imaging abnormalities (ARIA), and treatment adjustments. Longitudinal assessments included plasma biomarkers, amyloid PET, cognitive measures, and MRI findings. IRRs occurred in 14/108 participants (13.0
Olfactory dysfunction may provide a non-invasive marker of risk for cognitive decline and complement multidomain approaches to early detection and risk stratification. We aimed to examine longitudinal olfactory trajectories in relation to cognitive change and postmortem neuropathology. Using data from the Arizona Study of Aging and Neurodegenerative Disorders (AZSAND), cognitively unimpaired individuals without parkinsonism at enrollment (n = 922; mean age 76.9 ± 7.8 years; 33.8
Computerized cognitive training (CCT) and nutritional interventions may improve cognitive functioning in individuals with mild cognitive impairment (MCI). This study investigated whether a combination of CCT and online nutritional counseling can improve global cognition in individuals with MCI. We also evaluated differences between MCI-specified and active control versions of the interventions. In this 2 × 2 factorial randomized controlled trial (BrainFit-Nutrition study), conducted fully remotely, participants in Germany with a psychometric diagnosis of MCI (age 60+) were randomized into four groups (Intention to treat, n = 270). For each intervention, one MCI specified and one active control version were developed (cognition: individualized CCT (iCCT) vs. basic CCT (bCCT); nutrition: whole-food plant-based nutrition (WFPB) vs. German Nutrition Society (DGE)). At baseline and after six months, cognitive functioning was assessed with the Montreal Cognitive Assessment (MoCA; primary outcome: change after six months). A mixed-model ANCOVA (t0-t6, complete case strategy (CC), n = 228) was applied for the main analysis. All four groups achieved a significant improvement of approximately 2 points on the MoCA after six months. Estimated marginal means varied from M = 2.44 (iCCTxDGE) to M = 1.77 (iCCTxWFPB), notably without significant group differences. The 2 × 2 mixed ANCOVA revealed a significant main effect of time (F = 3.96, p = 0.048, partial η2 = 0.018). The specific content of the respective modalities being compared (iCCT vs. bCCT; WFPB vs. DGE-oriented counseling) did not appear to make a relevant group difference with regard to the primary outcome. After 6 months, 55
