Dutch researchers developed SemaGBA, a computational model integrating 14 metabolic and neural variables to map semaglutide's mechanisms of action. The model accurately predicted clinical outcomes: 38.0 mg/dL glucose reduction and 3.2 kg weight loss for diabetes patients, plus 15.1% weight loss for obesity treatment. Beyond validating known effects, the model revealed how semaglutide simultaneously targets metabolic pathways—reducing lipotoxicity and glucotoxicity while preserving β-cell function—and neural circuits involving AgRP, POMC, and dopamine neurons that control appetite. This dual-axis approach represents a sophisticated advance in understanding GLP-1 receptor agonists beyond their immediate clinical effects. The modeling framework offers particular value for precision medicine, potentially identifying which patients might benefit most from early intervention. When simulated during prediabetes, semaglutide prevented diabetes progression by maintaining β-cell function. While computational models have inherent limitations and require clinical validation, this systems-level approach provides a roadmap for next-generation metabolic therapeutics that could simultaneously target multiple pathways. The work suggests that semaglutide's remarkable clinical success stems from coordinated intervention across traditionally separate physiological domains—metabolism and neurobehavior.