(309f) A Multiscale Model of Acute Insulin Resistance in Critical Illness

Robust blood glucose regulation is an evolutionarily conserved mechanism present in many living organisms and vital to the survival of a species. These mechanisms have developed aggressive measures to prevent blood glucose from falling below threshold (hypoglycemia), while loosely regulating overshoot, which results in hyperglycemia that has potentially harmful, but less immediate, consequences. The immune response plays an important role in this regulation by overriding insulin-triggered glucose uptake to provide resources needed to fight against infection and starvation¹. The immune activation hijacking of the body’s metabolism, while evolutionarily rational for survival, induces a potentially harmful increase in glucose levels via insulin resistance. Elevated blood glucose following injury, so called “stress hyperglycemia” or “diabetes of injury”, has been correlated with increased mortality rates in the ICU². We aim to develop a model-based decision support system (glucose sensor, control algorithm, insulin pump, user-friendly interface) to maintain glucose homeostasis without hypoglycemia in critically ill patients. Key to the control algorithm component is a multiscale system model linking cellular response to systemic response. This does not presently exist.

We synthesize a dynamic model of glucose metabolism in relation to the acute immune response by focusing on three submodels. Our multi-scale model consists of: (i) a cellular level model of the insulin-action pathway³; (ii) a compartmental model describing tissue-level interaction⁴; and (iii) a novel data-driven model describing the immune response to injury. At the cellular level the antagonist(s) of insulin sensitivity deactivate the tyrosine phosphorylation cascade, following insulin binding, through serine phosphorylation⁵. We use the detailed mechanistic model proposed by Sedaghat and colleagues as a functioning insulin-responsive cell to which we add a state corresponding to the serine kinase population that acts to inhibit insulin action. The cellular model receives input from the injury model in the form of increased serine kinase levels, as well as extravascular insulin from the tissue model. The cellular insulin-responsive glucose uptake is fed back into the tissue-level model as a variable glucose clearance rate. The tissue-level model from Roy⁴also accounts for exercise and fatty acids which can be used as inputs for the injury model.

The injury model characterizes the immune response to trauma using biometrics, cytokines and stress-hormones as inputs that are believed to influence serine kinase activity. Data from APACHE III scores (a recognized metric describing severity of illness⁶) are used as inputs that have been correlated to insulin sensitivity⁷. Fatty acids from the tissue model are classified by their inflammatory characteristics and accordingly propagate the immune response. Stress-induced hormones such as catecholamines and glucocorticoids⁸also influence insulin resistance, and these are described in the injury model as additional inputs to the cellular model to attenuate insulin-dependent glucose uptake.

The wide range of patient conditions and biomolecules contributing to stress hyperglycemia makes accurate characterization a serious challenge. The composite model allows us to depict the overall dynamics of glucose metabolism while detailing important molecular events. Analysis at the cellular level allows us to identify dynamic micro-scale factors influencing biological regulation of systemic glucose and insulin in patients recovering from trauma. Using multiple scales we are able to describe key phenomena relevant to glucose control, as applied to critical care patients. Molecular-level insight into stress hyperglycemia could reveal key aspects of patient specific acute insulin resistance which could be used in a model-based decision support system for controlling glucose in the intensive care unit.

1. Fernandez-Real, J.-M., et al. (1999). Insulin Resistance and Inflammation in an Evolutionary Perspective: the Contribution of Cytokine Genotype/Phenotype to Thriftiness. Diabetologia,42: 1367-1374

2. Van den Berghe, G. et al., (2001). Intensive Insulin Therapy in Critically Ill Patients. The New England Journal of Medicine. 345:1359-1367.

3. Sedaghat, A. R. et al. (2002). A Mathematical Model of Metabolic Insulin Signaling Pathways. American Journal of Physiology Endocrinology and Metabolism, 283: E1084-E11101

4. Roy, Anirban (2008). Dynamic Modeling of Free Fatty Acid, Glucose, and Insulin During Rest and Exercise in Insulin Dependent Diabetes Mellitus Patients. Ph.D dissertation, University of Pittsburgh. 

5. Kitano, H. et al. (2004). Metabolic Syndrome and Robustness Tradeoffs. Diabetes,53 Suppl. 3: S6-S15.

6. Knaus, W. et al., (1991). The APACHE III Prognostic System. CHEST. 100(6): 1619-1636.

7. Zauner, A. et al. (2007). Severity of Insulin Resistance in Critically Ill Medical Patients. Metabolism. 56(1): 1-5.

8. Li, L. et al. (2009). Acute Insulin Resistance Following Injury. Trends in Endocrinology and Metabolism. 20(9): 429-435.