Search for: All records

Alzheimer’s disease (AD) presents significant challenges in clinical practice due to its heterogeneous manifestation and variable progression rates. This work develops a comprehensive anatomical staging framework to predict progression from mild cognitive impairment (MCI) to AD. Using the ADNI database, the scalable Subtype and Stage Inference (s-SuStaIn) model was applied to 118 neuroanatomical features from cognitively normal (n = 504) and AD (n = 346) participants. The framework was validated on 808 MCI participants through associations with clinical progression, CSF and FDG-PET biomarkers, and neuropsychiatric measures, while adjusting for common confounders (age, gender, education, and APOE ε4 alleles). The framework demonstrated superior prognostic accuracy compared to traditional risk assessment (C-index = 0.73 vs. 0.62). Four distinct disease subtypes showed differential progression rates, biomarker profiles (FDG-PET and CSF Aβ42), and cognitive trajectories: Subtype 1, subcortical-first pattern; Subtype 2, executive–cortical pattern; Subtype 3, disconnection pattern; and Subtype 4, frontal–executive pattern. Stage-dependent changes revealed systematic deterioration across diverse cognitive domains, particularly in learning acquisition, visuospatial processing, and functional abilities. This data-driven approach captures clinically meaningful disease heterogeneity and improves prognostication in MCI, potentially enabling more personalized therapeutic strategies and clinical trial design. 

Alzheimer’s disease (AD) is a complex and progressive neurodegenerative condition with significant societal impact. Understanding the temporal dynamics of its pathology is essential for advancing therapeutic interventions. Empirical and anatomical evidence indicates that network decoupling occurs as a result of gray matter atrophy. However, the scarcity of longitudinal clinical data presents challenges for computer-based simulations. To address this, a first-principles-based, physics-constrained Bayesian framework is proposed to model time-dependent connectome dynamics during neurodegeneration. This temporal diffusion network framework segments pathological progression into discrete time windows and optimizes connectome distributions for biomarker Bayesian regression, conceptualized as a learning problem. The framework employs a variational autoencoder-like architecture with computational enhancements to stabilize and improve training efficiency. Experimental evaluations demonstrate that the proposed temporal meta-models outperform traditional static diffusion models. The models were evaluated using both synthetic and real-world MRI and PET clinical datasets that measure amyloid beta, tau, and glucose metabolism. The framework successfully distinguishes normative aging from AD pathology. Findings provide novel support for the “decoupling” hypothesis and reveal eigenvalue-based evidence of pathological destabilization in AD. Future optimization of the model, integrated with real-world clinical data, is expected to improve applications in personalized medicine for AD and other neurodegenerative diseases. 

Background: Amyloid-β plaques (Aβ) are associated with Alzheimer’s disease (AD). Pooled assessment of amyloid reduction in transgenic AD mice is critical for expediting anti-amyloid AD therapeutic research. Objective: The mean threshold of Aβ reduction necessary to achieve cognitive improvement was measured via pooled assessment (n = 594 mice) of Morris water maze (MWM) escape latency of transgenic AD mice treated with substances intended to reduce Aβ via reduction of beta-secretase cleaving enzyme (BACE). Methods: Machine learning and statistical methods identified necessary amyloid reduction levels using mouse data (e.g., APP/PS1, LPS, Tg2576, 3xTg-AD, control, wild type, treated, untreated) curated from 22 published studies. Results: K-means clustering identified 4 clusters that primarily corresponded with level of Aβ: untreated transgenic AD control mice, wild type mice, and two clusters of transgenic AD mice treated with BACE inhibitors that had either an average 25% “medium reduction” of Aβ or 50% “high reduction” of Aβ compared to untreated control. A 25% Aβ reduction achieved a 28% cognitive improvement, and a 50% Aβ reduction resulted in a significant 32% improvement compared to untreated transgenic mice (p < 0.05). Comparatively, wild type mice had a mean 41% MWM latency improvement over untreated transgenic mice (p < 0.05). BACE reduction had a lesser impact on the ratio of Aβ42 to Aβ40. Supervised learning with an 80% –20% train-test split confirmed Aβ reduction was a key feature for predicting MWM escape latency (R2 = 0.8 to 0.95). Conclusions: Results suggest a 25% reduction in Aβ as a meaningful treatment threshold for improving transgenic AD mouse cognition. 

Alzheimer’s disease has a prolonged asymptomatic phase during which pathological changes accumulate before clinical symptoms emerge. This study aimed to stratify the risk of clinical disease to inform future disease-modifying treatments. Cerebrospinal fluid analysis from participants in the Emory Healthy Brain Study was used to classify individuals based on amyloid beta 42 (Aβ42), total tau (tTau) and phosphorylated tau (pTau) levels. Cognitively normal (CN), biomarker-positive (CN)/BM+individuals were identified using a tTau: Aβ42 ratio > 0.24, determined by Gaussian mixture models. CN/BM+ individuals (n = 134) were classified as having asymptomatic Alzheimer’s disease (AsymAD), while CN, biomarker-negative (CN/BM−) individuals served as controls (n = 134). Cognitively symptomatic, biomarker-positive individuals with an Alzheimer’s disease diagnosis confirmed by the Emory Cognitive Neurology Clinic were labelled as Alzheimer’s disease (n = 134). Study groups were matched for age, sex, race and education. Cerebrospinal fluid samples from these matched Emory Healthy Brain Study groups were analysed using targeted proteomics via selected reaction monitoring mass spectrometry. The targeted cerebrospinal fluid panel included 75 peptides from 58 unique proteins. Machine learning approaches identified a subset of eight peptides (ADQDTIR, AQALEQAK, ELQAAQAR, EPVAGDAVPGPK, IASNTQSR, LGADMEDVCGR, VVSSIEQK, YDNSLK) that distinguished between CN/BM− and symptomatic Alzheimer’s disease samples with a binary classifier area under the curve performance of 0.98. Using these eight peptides, Emory Healthy Brain Study AsymAD cases were further stratified into ‘Control-like’ and ‘Alzheimer’s disease-like’ subgroups, representing varying levels of risk for developing clinical disease. The eight peptides were evaluated in an independent dataset from the Alzheimer’s Disease Neuroimaging Initiative, effectively distinguishing CN/BM− from symptomatic Alzheimer’s disease cases (area under the curve = 0.89) and stratifying AsymAD individuals into control-like and Alzheimer’s disease-like subgroups (area under the curve = 0.89). In the absence of matched longitudinal data, an established cross-sectional event-based disease progression model was employed to assess the generalizability of these peptides for risk stratification. In summary, results from two independent modelling methods and datasets demonstrate that the identified eight peptides effectively stratify the risk of progression from asymptomatic to symptomatic Alzheimer’s disease. 

Background: The complex and not yet fully understood etiology of Alzheimer’s disease (AD) shows important proteopathic signs which are unlikely to be linked to a single protein. However, protein subsets from deep proteomic datasets can be useful in stratifying patient risk, identifying stage dependent disease markers, and suggesting possible disease mechanisms. Objective: The objective was to identify protein subsets that best classify subjects into control, asymptomatic Alzheimer’s disease (AsymAD), and AD. Methods: Data comprised 6 cohorts; 620 subjects; 3,334 proteins. Brain tissue-derived predictive protein subsets for classifying AD, AsymAD, or control were identified and validated with label-free quantification and machine learning. Results: A 29-protein subset accurately classified AD (AUC = 0.94). However, an 88-protein subset best predicted AsymAD (AUC = 0.92) or Control (AUC = 0.92) from AD (AUC = 0.98). AD versus Control: APP, DHX15, NRXN1, PBXIP1, RABEP1, STOM, and VGF. AD versus AsymAD: ALDH1A1, BDH2, C4A, FABP7, GABBR2, GNAI3, PBXIP1, and PRKAR1B. AsymAD versus Control: APP, C4A, DMXL1, EXOC2, PITPNB, RABEP1, and VGF. Additional predictors: DNAJA3, PTBP2, SLC30A9, VAT1L, CROCC, PNP, SNCB, ENPP6, HAPLN2, PSMD4, and CMAS. Conclusion: Biomarkers were dynamically separable across disease stages. Predictive proteins were significantly enriched to sugar metabolism.