Type 2 Diabetes (T2D) is a heterogeneous metabolic disorder driven by the interplay between endocrine stress and glucose regulation. We adapt an 18-variable ordinary differential equation (ODE) model of the hypothalamic-pituitary-adrenal (HPA) and metabolic systems to a UK Biobank pilot cohort (N = 100) representing metabolic extremes (50 healthy, 50 T2D). Scaling mechanistic models is challenged by inherent population metabolic diversity. To address this, we implement an individualized scaling framework anchoring glucose uptake and hepatic production to baseline fasting glucose. Results demonstrate that this approach reproduces clinical bimodal distributions and phenotypic attractors without manual refitting. Validation against independently observed HbA1c (r = 0.82, p < 0.0001, n = 95) confirms that the simulated steady states track a clinically meaningful glycemic marker not used during model initialization. By achieving statistical parity using only baseline glucose and mental health data, this framework enables scaling neuroendocrine-metabolic models to the full UK Biobank for virtual trials and personalized interventions.