Obesity: an evolutionary context

Abstract

People completely lacking body fat (lipodystrophy/lipoatrophy) and those with severe obesity both show profound metabolic and other health issues. Regulating levels of body fat somewhere between these limits would, therefore, appear to be adaptive. Two different models might be contemplated. More traditional is a set point (SP) where the levels are regulated around a fixed level. Alternatively, dual-intervention point (DIP) is a system that tolerates fairly wide variation but is activated when critically high or low levels are breached. The DIP system seems to fit our experience much better than an SP, and models suggest that it is more likely to have evolved. A DIP system may have evolved because of two contrasting selection pressures. At the lower end, we may have been selected to avoid low levels of fat as a buffer against starvation, to avoid disease-induced anorexia, and to support reproduction. At the upper end, we may have been selected to avoid excess storage because of the elevated risks of predation. This upper limit of control seems to have malfunctioned because some of us deposit large fat stores, with important negative health effects. Why has evolution not protected us against this problem? One possibility is that the protective system slowly fell apart due to random mutations after we dramatically reduced the risk of being predated during our evolutionary history. By chance, it fell apart more in some people than others, and these people are now unable to effectively manage their weight in the face of the modern food glut. To understand the evolutionary context of obesity, it is important to separate the adaptive reason for storing some fat (i.e. the lower intervention point), from the nonadaptive reason for storing lots of fat (a broken upper intervention point). The DIP model has several consequences, showing how we understand the obesity problem and what happens when we attempt to treat it.

Keywords: BMI; adaptive; adiposity; body fat; dual-intervention point model; evolution; leptin resistance; metabolic programming; obesity; selection; set-point model.

Figure 1

Two different models of body fat regulation. (a) SP system. Deviations of fatness above and below the regulated SP invoke strong physiological responses to resist the change. Elevations above the SP lead to reduced intake and increased expenditure, while the converse happens when fatness falls below the SP. (b) DIP system. Now there are two intervention points on the spectrum of body fatness: a lower point below which physiology resists further loss and an upper point above which physiology resists further gain. Between the two is a zone of indifference where weight is not physiologically regulated

Figure 2

Two ideas why we become obese in modern society based on the models in Fig. 1. (a) Under the SP model, obesity develops because there is a weakening of the physiological regulatory response to increasing leptin levels, which has been termed “leptin resistance.” In the face of an environmental push, the SP is no longer able to resist weight gain. (b) Under the DIP model, it is suggested that the position of the upper intervention point has drifted in evolutionary time (the “drifty gene” hypothesis); hence, under an environmental push, individuals become obese to differing extents until they reach their own intervention points. The absence of a response to leptin at the LIP is not, therefore, seen as a pathological state of “leptin resistance” but part of how the system always works

Figure 3

Three patterns of linkage disequilibrium (Logarithm of the odds scores) for single-nucleotide polymorphisms (SNPs) surrounding (200 kb upstream and downstream) three example target SNPs. The pattern, in (A), is an SNP (rs4988235) adjacent to the lactase gene that shows a significant association between the target and other SNPs over a wide surrounding distance. This is indicative of a selective sweep reflecting strong positive selection in this region of the genome. The pattern, in B, for a typical obesity-related SNP (rs1499209) by contrast shows a sharp peak around the target indicative of no strong selection and similar to the pattern, in C, for a randomly selected SNP (rs1420258) (data from the International HapMap Consortium: presented in [198])