Abstract
Modern biomedicine frequently contextualizes disease around isolated molecular or organ-specific mechanisms, but numerous chronic diseases, including Alzheimer's disease, multiple sclerosis, depression, diabetes, and sepsis, share common trajectories of systemic destabilization. An increasing body of evidence indicates that health is not a property of single organs but the emergent property of interdependent feedback networks linking the microbiome, endocannabinoidome, neuroimmune system, and metabolic regulators. We propose the Endocannabinoid-Microbiota-Neuroimmune Super-System (EMN-S) as an evolutionarily conserved conceptual model that describes how these fields of influence reciprocally interact through feedback control. The microbial communities constituting the EMN-S encode environmental and dietary inputs, endocannabinoid signaling serves as an integrative regulator that synchronizes neural and immune activity, and neuroimmune circuits effectuate adaptive behaviors that alter microbiotal and lipid ecosystems. This review formalizes the EMN-S, contending that it is a unitary and cohesive model of physiological resilience, as well as offering a framework for precision feedback therapeutics. We describe how three mechanisms-encoder drift, integrator detuning, and executor overutilization-convert stabilizing negative feedback into runaway feedback cascades that underlie chronic, recurrent, and multisystemic disease. We then specify the EMN-S signature-integrated microbiome, lipidomic, and immune readouts-as an early indicator of resilience collapse and prospective preclinical state. Finally, we recapitulate the potential of AI-driven digital twins to illuminate feedback collapse, predict tipping points, and direct closed-loop intervention and treatments to restore dynamic equilibrium. By anchoring complexity in concrete and measurable feedback principles, the EMN-S shifts focus to investigate pathophysiology as opposed to reductionist lesion models of systemic derangements and embraces a systemic, empirically testable theory of stability.