Objectives

Body fatness is widely assumed to be regulated by a lipostatic set-point system, which has evolved in response to trade-offs in the risks of mortality. Increasing fatness makes the risk of starvation lower but increases the risk of predation. Yet other models are available. The aim of this work is to evaluate using mathematical modeling whether set-point systems are more likely to evolve than the alternatives.

 

Methods

I modeled the trade-off in mortality risks using a simple mathematical model, which generates an optimum level of fatness that is presumed to be the driver for the evolution of a set-point. I then mimicked the likely errors in this optimum level, that derive from the variation in the component parameters of the mortality curves using Markov Chain Monte Carlo (MCMC) simulation by Bayesian inference Using Gibbs Sampling (BUGS).

 

Results

The error propagation generated by the simulations showed that even very small errors in the model parameters were magnified enormously in the location of the optimum fatness level. If the model parameters had coefficients of variation of just 1% then the coefficient of variation in the optimum level of fatness was between 20 and 90%. In that situation, a set-point centered at the mathematical optimum from the component curves would be at the correct level of fatness that minimizes mortality, and hence maximizes fitness, on less than 8% of occasions.

 

Conclusions

Set-point regulation of body fatness is hence highly unlikely to evolve where there is any realistic level of variation in the parameters that define mortality risks. Using further MCMC modeling, I show that a dual-intervention point system is more likely to evolve. This mathematical simulation work has important implications for how we interpret molecular work concerning regulation of adiposity.