Multivariate blood biomarkers capture resilience and resistance phenotypes across the Alzheimers disease spectrum

Alzheimers disease blood biomarkers reveal proteomic modules of disease progression

Routine and accessible plasma measures can be leveraged to recover reproducible, biologically distinct progression modules that improve characterization of heterogeneous AD and have practical value for risk stratification, trial enrichment, or treatment monitoring.

Comparative analyses of Alzheimer's disease blood biomarkers and cognitive domains

Abstract INTRODUCTION Whether Alzheimer's disease (AD) blood biomarker–cognition associations differ across cognitive domains, analytic context, and biomarker modeling strategy in population‐based cohorts is unclear. METHODS In 1170 older adults from the Health and Retirement Study Harmonized Cognitive Assessment Protocol (HRS‐HCAP), we examined cross‐sectional (2016) and prospective (2016 to 2022) associations of blood phosphorylated tau at threonine 181 (p‐tau181), glial fibrillary acidic protein (GFAP), neurofilament light (NfL), and amyloid beta 42/40 ratio (Aβ42/40) with memory, executive function, language, visuospatial ability, and global cognition using individual‐biomarker, principal component analysis‐derived composite, and multi‐biomarker panel models. RESULTS Cross‐sectionally, NfL and GFAP showed the broadest associations with cognitive performance. In simultaneous models of baseline‐adjusted follow‐up cognition, p‐tau181 was associated with lower memory (β = −0.080, q < 0.001) and global cognition (β = −0.066, q < 0.001), while GFAP was associated with lower executive function (β = −0.047, q = 0.007), memory (β = −0.047, q = 0.032), and global cognition (β = −0.066, q = 0.002). NfL and Aβ42/40 were not independently associated after mutual adjustment. P‐tau181 also showed relative domain selectivity between memory and executive function. Relative model support differed by cognitive domain and analytic context. DISCUSSION AD blood‐based biomarker–cognition associations were domain‐differentiated and context‐dependent: Concurrent associations did not reliably indicate associations with cognition 6 years later, and only p‐tau181 showed formal evidence of relative memory‐versus‐executive selectivity. Exploratory pairwise comparisons highlighted p‐tau181 + GFAP for follow‐up memory and global cognition, warranting independent evaluation.

Multimodal risk trajectories reveal heterogeneous paths to dementia

Dementia comprises biologically heterogeneous disorders, yet current risk assessment provides limited insight into how subtype-specific risk emerges and diverges before clinical diagnosis. We developed NetMoint, a multimodal framework integrating partially observed plasma proteomic, structural magnetic resonance imaging and cerebral haemodynamic phenotypes to predict individualized risks of Alzheimer's disease (AD), vascular dementia (VD) and frontotemporal dementia (FTD) across 1-, 5-, 10- and 20-year horizons. Among 104,120 UK Biobank participants free of dementia at baseline, NetMoint achieved mean area under the receiver operating characteristic curve (AUC) values of 0.937, 0.930 and 0.932 for AD, VD and FTD, respectively. The biological determinants of prediction shifted with time, from structural brain vulnerability at shorter horizons towards circulating molecular signatures at longer horizons, with distinct subtype-specific biological profiles. Multi-horizon risk profiling identified distinct temporal trajectories of dementia susceptibility. Among participants who subsequently developed AD, 0.7% followed a persistently very-high-risk trajectory, with predicted risk reaching 53.50% at 20 years, whereas 8.3% of those who developed FTD followed an increasing very-high-risk trajectory, reaching 67.17%. These high-risk trajectories were marked by distinct molecular signatures, with lower TGFB1 characterizing the AD group and higher NDRG1 the FTD group. In an independent ADNI-to-UK Biobank analysis, AD risk prediction remained informative after harmonization to 138 shared features, with an AUC of 0.741 at 20 years. Together, these findings establish a multimodal framework for trajectory-resolved dementia risk stratification, identifying small but high-risk populations within dementia subtypes and linking their divergent risk trajectories to distinct molecular signatures.

Alzheimer’s Disease and Cognitive Disorders: From Molecular Drivers to Multilevel Clinical Solutions

Abstract Alzheimer’s disease (AD) represents the leading cause of dementia worldwide, accounting for approximately 70% of all dementia cases, and is characterized by progressive cognitive decline arising from complex interactions among molecular, genetic, environmental, and systemic factors. This review integrates current mechanistic insights with evolving diagnostic and therapeutic approaches. Central pathogenic processes include amyloid-β (Aβ) aggregation, tau hyperphosphorylation and propagation, neuroimmune dysregulation, synaptic failure, and network-level circuit disruption. Genetic and epigenetic contributors such as APOE genotype, aging-related inflammatory signaling, oxidative stress, and lifestyle-associated risk factors further modify susceptibility and disease trajectory. Recent advances in fluid biomarkers, amyloid and tau molecular imaging, and digital phenotyping—such as AI-driven cognitive assessments, gait analysis, voice analytics, and passive activity monitoring—have improved early detection and patient stratification. Emerging therapeutic strategies include disease-modifying agents, neuroprotective interventions, neurotrophin mimetics, and hormesis-based lifestyle approaches such as structured exercise, caloric modulation, and intermittent fasting. Precision-medicine trial designs and biomarker-guided therapeutic selection are transforming clinical research. At the clinical and public health level, caregiver-centered care models, community-based support systems, and multidomain prevention strategies emphasize the need for equitable, culturally informed dementia care. Future progress will depend on large-scale, diverse cohort studies, multi-omics integration, single-cell brain atlases, and AI-supported neuropathology pipelines to enable prevention and intervention during the preclinical window. This review highlights the importance of bridging mechanistic research with real-world clinical practice to generate impactful diagnostic, therapeutic, and policy outcomes. Keywords: Alzheimer’s disease, amyloid-β and tau pathology, neurodegeneration, biomarkers and digital phenotyping, precision medicine.  

Joint impact of pathological burden and cognitive resilience on Alzheimer's disease risk.

Deep multimodal clustering identifies biological subtypes of normal aging with divergent cognitive decline risk