There is a paucity of data on the longitudinal changes of whole-body metabolism and body composition in patients with progressive cancer cachexia. Unfortunately, it would be prohibitively difficult and invasive to attempt a comprehensive study in such patients that includes measurements of whole body metabolic fluxes, energy expenditure, physical activity, food intake, and body composition change. In the absence of such a study, the best we can do is piece together information from separate studies to help understand this complex and serious disorder.
We used a computational model to integrate a variety of published data on the primary metabolic changes that occur in cancer cachexia: including increased proteolysis, lipolysis, Cori cycling, tumor growth, and maintenance of liver mass. We introduced these defects linearly over a 12 month period along with reductions of food intake and physical activity. These metabolic changes may be sufficient to explain the observed increases of RMR and GNG in cancer cachexia since the simulated RMR and GNG rates were consistent with existing data [31
] and were the downstream consequence of the primary metabolic parameter changes. Furthermore, the simulated relative changes of FFM and FM were consistent with body composition changes observed in cancer patients versus normal subjects [36
While computational models of cancer cachexia provide a useful conceptual framework for integrating clinical data, we caution against the use of such models to predict the clinical course of an individual patient. In fact, our model suggests that the high inter-individual variability of the primary metabolic defects, along with the variability of food intake, physical activity level and initial body composition, can have a significant impact on the projected severity and pattern of wasting. Since it would be impractical to attempt to measure all of these parameters in an individual patient, the computational model is best applied to help understand and predict average responses and investigate the sensitivity of these responses to changes in model parameters.
Computational models can be used to investigate the potential therapeutic benefits of various interventions. For example, our simulations offered an explanation of the clinical observation that orexigenic drug treatments or aggressive nutritional support tend to attenuate the rate of weight loss, but rarely result in significant weight gain or weight normalization [47
]. These conclusions underscore the limitations of applying nutritional support as a sole therapy since unabated metabolic changes can undermine their efficacy.
We also simulated the effects of inhibiting proteolysis and lipolysis as potential treatments for cancer cachexia. Importantly, the computational model is not a black box and was used to understand the physiological basis of each prediction. The simulations suggest that proteolysis inhibition may be a reasonable therapeutic target for cancer cachexia in patients with appreciable fat reserves. In contrast, inhibiting lipolysis may not be a good therapeutic target because the decreased supply of fatty acids during negative energy balance necessitated greater protein catabolism to meet the energetic needs and would likely result in significant deterioration of skeletal muscle mass.
Computational model simulations alone should not be used to rule out a potential therapeutic target; however such models provide a useful tool for clinical study design. For example, our simulation of lipolysis inhibition in cancer cachexia suggests that nitrogen balance and urinary creatinine measurements should be performed during any clinical trial of lipolysis inhibition since such measurements may provide an early indication of increased muscle wasting as predicted by the model. Thus, in addition to providing a conceptual framework for integrating clinical data, computational models are also an important tool for helping design clinical investigations as well as to improve our understanding of the complex metabolic state of cancer cachexia.