Mathematical models are playing an increasing role in understanding the complexity of multifactorial diseases like type 2 diabetes. The objective of this study was to validate a population of virtual patients against a real population of patients with type 2 diabetes. A population of virtual patients was created that incorporates different underlying pathogenic lesions consistent with a type 2 diabetic phenotype. These virtual patients were created within the Metabolism PhysioLab platform, a non-linear coupled differential algebraic model that incorporates the salient causal mechanisms underlying glucose homeostasis and substrate metabolism. The weights of each individual virtual patient were determined to reproduce the diversity in a real type 2 diabetic population obtained from the NHANES III study. As a validation test, this virtual population reproduced a series of clinical studies that identify less invasive biomarkers for insulin sensitivity. This approach demonstrates how computational bridges can be constructed between statistical approaches common in epidemiology and deterministic approaches common in biomedical engineering.