Background: Understanding type 2 diabetes is challenged by the diversity of patient phenotypes. Translating data across species and among individuals is a barrier for understanding the genetic loci that underpin this multifactorial disease. Dynamic scaling, based upon dimensional analysis, is a common technique in engineering used to translate data among different systems. The objective of this study was to gain qualitative insight into underlying biological phenomena responsible for glucose homeostasis using dimensional analysis.

Methods: A dimensionless number was derived using variables involved in the production of insulin and in the sensitivity of glucose metabolism to insulin. The resulting dynamic scaling relationship was validated against patient data obtained for over 2000 individuals that range in phenotype from normal to severe type 2 diabetes. Individuals were identified in the third National Health and Nutrition Evaluation Survey.

Results: Patient groups clustered in different regions based upon the severity of clinical symptoms. The cross-sectional comparison among patient groups shows that progression from normal to clinical onset of type 2 diabetes is consistent with a decrease in the sensitivity to insulin. However, a non-linear change in insulin production capacity was associated with an increase in severity in diabetic symptoms (i.e., normal to clinical diagnosis of type 2 diabetes).

Conclusions: This dimensionless number provides a method for discriminating between patient groups from first principles. By analogy with other dimensionless numbers, this number may be used to monitor basic physiological variables responsible for glucose homeostasis. In addition, similarity in dynamic trajectories could provide a criterion for selecting relevant animal models for diabetes.