Computational modeling of cancer cachexia

Abstract

Purpose of review-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 where invasive metabolic tests may be precluded. Thus, models of cancer-associated weight loss are an important tool for helping to understand this debilitating condition.Recent findings-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 and the resulting computer simulations show how the known metabolic derangements synergize with reduced energy intake to result in a progressive loss of body weight (BW), fat mass (FM), and fat-free mass (FFM). 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.Summary-Computational modeling is a new tool that can integrate clinical data on the metabolic changes in cancer cachexia and provides a conceptual framework to help understand involuntary weight loss and predict the effects of potential therapies.

A viscerally driven cachexia syndrome in patients with advanced colorectal cancer: contributions of organ and tumor mass to whole-body energy demands

“…Model simulations of the healthy reference condition (dotted curves) compared with reduced energy intake alone (dashed dotted curves), our previous cachexia simulation (14) (dashed curves), and the new cachexia simulation based on the directly determined liver and spleen masses of our retrospective colorectal cancer cohort are illustrated in Figure 5 (from Table 1 ) above the previous cachexia simulation. Notably, the late rapid increase in liver mass measured in the retrospective colorectal cancer patient cohort was related to a steep increase in estimated metabolic rate during the last 3 mo of the simulation.…”

“…This model was also used to integrate data on the metabolic changes in patients with cancer cachexia to show how these derangements synergize with reduced energy intake to result in progressive loss of body constituents and alterations in energy metabolism (14). The initial conditions of the cachexia simulation (14) were selected to represent a typical cancer patient before disease onset: a 69-y-old male with an initial body weight of 77.7 kg, 32% body fat, a dietary intake of 2400 kcal/d, and an REE of 1606 kcal/d (30.6 kcal Á kg FFM 21 Á d 21 ).…”

“…This model was also used to integrate data on the metabolic changes in patients with cancer cachexia to show how these derangements synergize with reduced energy intake to result in progressive loss of body constituents and alterations in energy metabolism (14). The initial conditions of the cachexia simulation (14) were selected to represent a typical cancer patient before disease onset: a 69-y-old male with an initial body weight of 77.7 kg, 32% body fat, a dietary intake of 2400 kcal/d, and an REE of 1606 kcal/d (30.6 kcal Á kg FFM 21 Á d 21 ). In the previously reported simulation (15), we applied several reported metabolic derangements defining cancer cachexia, including an increase in the rates of lipolysis (50 6 30%), proteolysis (40 6 10%), and Cori cycle (300 6 100%); a tumor mass of 200 6 100 g; and an energy intake that linearly decreased to 1700 kcal/d by the end of the 12-mo simulation.…”

“…A retrospective serial computed tomography (CT) image analysis was completed to assess longitudinal body-composition changes (ie, liver, spleen, skeletal muscle, and adipose tissue) in subjects with colorectal cancer. Using data from this cohort, we used a computational model of human metabolism (13,14) to estimate the effect of organ changes on wholebody REE. In a second cross-sectional cohort, we evaluated the relation between REE determined by indirect calorimetry and organ mass.…”

The challenges for molecular nutrition research 4: the “nutritional systems biology level”

“…These examples include the kinetic analysis and even multicompartment models of lipoprotein metabolism [7,21], or even computational models to analyse human energy metabolism during semi-starvation and in cancer cachexia [9,10]. Examples to describe mammalian nutrient metabolism in quantitative kinetic models have been put forward for folate and most recently for glutathione [22,23].…”