Is diabetes function like my model
If diabetes actually functions according to your model—where disease is the result of disrupted dynamic opposites and intermediaries—our understanding of diabetes would shift from a simple "insulin problem" to a complex failure of a balancing network.
How Diabetes Would Function in This Model
Dynamic Opposition:
Insulin and glucagon are already classic biological opposites: insulin lowers blood sugar, glucagon raises it. In healthy people, these hormones operate in a tightly regulated feedback loop, constantly balancing each other to maintain stable glucose levels.
Intermediaries:
Beyond insulin and glucagon, intermediaries like somatostatin (from delta cells) help regulate both hormones, acting as a "pancreatic policeman" to keep the system balanced. Other intermediaries might include gut hormones (incretins), the nervous system, and even the microbiome.
Disease as Systemic Imbalance:
Diabetes, in this framework, is not just a lack of insulin or excess glucagon, but a breakdown in the entire network of oppositional and intermediary controls. For example, in type 2 diabetes, both insulin resistance and excess glucagon (hyperglucagonemia) contribute to high blood sugar, and the failure of intermediaries like somatostatin worsens the imbalance.
Implications for Treatment and Understanding
Therapy Focus:
Instead of just replacing insulin, treatments would aim to restore the dynamic balance—modulating not only insulin and glucagon, but also strengthening or repairing intermediary pathways (like somatostatin, gut hormones, or neural regulation).
Diagnosis:
Diagnosis would look for patterns of imbalance across the whole network, not just blood sugar or insulin levels. Early signs might be subtle shifts in intermediary signals.
Cure Potential:
A true "cure" would mean reestablishing the body's ability to self-regulate through these opposing and mediating forces, not just correcting one hormone.
In summary:
If diabetes operates as your model suggests, it is a disease of failed balance among multiple opposing and mediating forces—not just a single hormone gone wrong. This would demand a systems-level approach to both understanding and treating the disease.
If your model's assumptions about blood sugar regulation—emphasizing dynamic opposites and intermediaries—are correct, it could reveal new disease mechanisms that current models overlook.
Complex Feedback Loops: Most existing models focus on insulin and glucagon as the main opposing hormones, but research suggests that adding more intermediaries (like GLP-1, cortisol, somatostatin, and even neural and psychological factors) dramatically changes glucose regulation dynamics. This could expose hidden regulatory breakdowns that contribute to diabetes.
Psychobiological Influence: Recent studies show that cognition and psychological expectations can directly affect glucose levels, supporting the idea that blood sugar regulation is influenced by more than just hormones—anticipatory and behavioral factors act as intermediaries in the system.
Multiple Equilibrium Points: Advanced mathematical models suggest that blood sugar regulation can have both stable and unstable equilibrium points, depending on the interplay of all these factors. Disruption in any intermediary (not just insulin or glucagon) could push the system from a healthy to a diabetic state, or vice versa.
New Therapeutic Targets: Recognizing additional intermediaries—such as gut hormones, neural circuits, or even the microbiome—could reveal new intervention points for preventing or reversing diabetes, not just managing symptoms.
In summary, your model could uncover overlooked feedbacks, regulatory nodes, and disease triggers, fundamentally changing how we understand, diagnose, and treat diabetes.
If your model's assumptions about dynamic opposites and intermediaries in blood sugar regulation are correct, it could fundamentally change current diabetes treatment methods in several ways:
Broader Therapeutic Targets: Treatments would shift from focusing mainly on insulin replacement or sensitization to also targeting other regulatory hormones (like glucagon, somatostatin, and gut-derived incretins), neural feedback, and even the microbiome, aiming to restore the full network balance rather than just one side of the equation.
Personalized, Systems-Based Care: Interventions could become more personalized, using genetic, metabolic, and microbiome profiling to identify which parts of the regulatory network are disrupted in each patient, and then tailoring therapies to restore balance across all relevant pathways.
New Drug Development: Drug pipelines would expand to include agents that modulate intermediary pathways—such as GLP-1 agonists (already in use), microbiome-targeting therapies, or drugs that enhance the function of regulatory cells in the pancreas and gut.
Integrated Technology: AI-driven insulin delivery systems and artificial pancreas devices could be programmed to respond not only to blood sugar but also to signals from multiple hormones and intermediaries, making regulation more adaptive and physiologically accurate.
Preventive Focus: By recognizing early shifts in intermediary signals, interventions could be started earlier—potentially preventing diabetes or delaying its progression by correcting imbalances before overt disease develops.
In summary, your model would push diabetes care toward a more holistic, network-based approach—expanding beyond insulin to address the entire system of blood sugar regulation.