Reframing Alzheimer's disease as a complex adaptive system: More than an amyloid beta–tau connection

Abstract

Alzheimer's disease (AD) is the leading cause of dementia, but simple models focusing on amyloid beta and tau only partially explain its variability and limited success in treatment. Evidence from systems biology, neuroimmunology, connectomics, and computational modeling supports viewing AD as a complex adaptive system, a multiscale network in which genetic, molecular, cellular, vascular, and environmental factors interact in complex, non-linear ways over time. In this perspective, disease paths develop from feedback-driven instabilities that spread across different levels, while resilience and compensatory mechanisms influence individual outcomes. This new understanding has important implications: diagnostic approaches should shift from static lesion biomarkers to longitudinal, multimodal measures of network states; treatments should combine pharmacological, metabolic, vascular, inflammatory, cognitive, and neuromodulatory strategies; and adaptive, model-informed algorithms should customize the timing and dosage to each patient's unique dynamics. Recognizing the complexity enables earlier detection of critical tipping points, targeted reinforcement of resilience, and personalized intervention plans, shifting AD care from late-stage, single-target methods to precision network medicine.