Computational modeling of cancer cachexia

Abstract

Measurements of whole-body energy expenditure, body composition, and in-vivo metabolic fluxes are required to quantitatively understand involuntary weight loss in cancer cachexia. Such studies are rare because cancer cachexia occurs near the end of life when invasive metabolic tests may be precluded. Thus, models of cancer-associated weight loss are an important tool for helping to understand this debilitating condition. A computational model of human macronutrient metabolism was recently developed that simulates the normal metabolic adaptations to semi-starvation and re-feeding. Here, this model was used to integrate data on the metabolic changes in patients with cancer cachexia. The resulting computer simulations show how the known metabolic disturbances synergize with reduced energy intake to result in a progressive loss of body weight, fat mass, and fat-free mass. The model was also used to simulate the effects of nutritional support and investigate inhibition of lipolysis versus proteolysis as potential therapeutic approaches for cancer cachexia. Computational modeling is a new tool that can integrate clinical data on the metabolic changes in cancer cachexia and provide a conceptual framework to help understand involuntary weight loss and predict the effects of potential therapies.