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Validating a dimensionless number for glucose homeostasis in humans
1Department of Chemical Engineering, West Virginia University, Morgantown, P.O. Box 6102, Morgantown, WV 25606-6102, USA. david.klinke@mail.wvu.edu
Annals of Biomedical Engineering
|June 11, 2009
Summary
This study introduces a new dimensionless number to analyze type 2 diabetes progression. It reveals non-linear changes in insulin production versus glucose metabolism across disease stages, aiding in patient stratification and model selection.
Area of Science:
- Biomedical Engineering
- Metabolic Disease Research
- Physiology
Background:
- Type 2 diabetes (T2D) presents diverse patient phenotypes, complicating research and data translation across species and individuals.
- Identifying genetic factors for T2D is challenging due to its multifactorial nature.
- Dimensional analysis, a technique from engineering, offers a method for data translation between systems.
Purpose of the Study:
- To apply dimensional analysis to understand the relationship between insulin production capacity and insulin-dependent glucose metabolism in T2D.
- To derive a dimensionless number reflecting these physiological changes across disease progression.
- To validate this number against a large patient dataset.
Main Methods:
- A dimensionless number was formulated using variables related to insulin production and glucose metabolism sensitivity.
- This number was validated using cross-sectional data from over 2000 individuals from the National Health and Nutrition Examination Survey (NHANES).
- Patient groups were analyzed based on T2D severity, from normal to severe stages.
Main Results:
- The derived dimensionless number successfully discriminated between patient groups representing different stages of T2D.
- A non-linear change in the ratio of insulin production to insulin-dependent glucose metabolism was observed with disease progression.
- Normals showed a balanced ratio, pre-diabetics an increase, and clinical T2D patients a decrease.
Conclusions:
- The dimensionless number provides a novel method for discriminating T2D patient groups based on physiological principles.
- This metric can potentially monitor key variables in glucose homeostasis.
- Similar dynamic trajectories may aid in selecting appropriate animal models for diabetes research.
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