Microglial responses are increasingly recognized as important modulators of Alzheimer’s disease (AD) progression. The colony-stimulating factor 1 (CSF1) and CSF1 receptor (CSF1R) axis is essential for microglial survival and functional adaptation, yet its relationships with AD pathology and clinical progression in humans remain unclear. This study included 706 participants from the Alzheimer’s Disease Neuroimaging Initiative with CSF measurements of CSF1 and CSF1R. We systematically examined their cross-sectional and longitudinal associations with cognition, dementia risk, AD biomarkers, microglial activity, brain volume, and metabolism. Additionally, subgroup analyses based on A/T(N) classification were conducted to evaluate their relationships with pathological status, and receiver operating characteristic (ROC) analyses were performed to assess their discriminative performance. Mediation analyses were further used to investigate potential pathways linking amyloid burden, CSF1 and CSF1R-related microglial responses, tau pathology, and cognition. Higher CSF1 and CSF1R levels were associated with greater tau-related pathology and elevated microglia-related markers, including soluble Triggering Receptor Expressed on Myeloid Cells-2 (sTREM2). Higher baseline CSF1 was further associated with subsequent cognitive decline, increased incident dementia risk (HR = 1.325, 95
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
Background Terazosin (TZ) is widely prescribed for hypertension and benign prostatic hyperplasia. Recent studies suggest that TZ enhances glycolysis and may protect against neurodegenerative diseases.Methods We tested the hypothesis that TZ is neuroprotective in Alzheimer's disease (AD) in a yeast model, a mouse model, and two human datasets.Results We report four main results. First, TZ increased ATP levels in a Saccharomyces cerevisiae mutant with impaired metabolism, and reduced Amyloid-beta42 (A beta 42) aggregation. Second, in 5xFAD mice, TZ attenuated amyloid pathology and rescued cognitive impairments in spatial memory and interval timing. Third, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, AD patients taking glycolysis-enhancing drugs had a slower progression of both cognitive dysfunction and metabolic neuroimaging biomarkers (18 F-fluorodeoxyglucose positron emission tomography (FDG-PET)). Finally, in the Merative Marketscan dataset, patients taking glycolysis-enhancing drugs had lower risks of developing AD.Conclusion These data provide preliminary evidence that glycolysis-enhancing drugs have therapeutic potential in AD.
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
