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Bye : Albert-László Barabási, Ph.D.
A recent study reported that among people who carried a single copy of the high-risk allele for the FTO gene, which is associated with fat mass and obesity, the risk of obesity increased by 30%. The risk of obesity increased by 67% among people who carried two alleles, and on average they gained 3.0 kg (6.6 lb) or more.1 Given that approximately one sixth of the population of European descent is homozygous for this allele, this link between the FTO gene and obesity appears to be one of the strongest genotype–phenotype associations detected by modern genome-screening techniques.

That obesity has a genetic component is not surprising: researchers have long known that it often runs in .families. In this issue of the Journal, Christakis and Fowler suggest that friends have an even more important effect on a person's risk of obesity than genes do.2 The authors reconstructed a social network showing the ties between friends, neighbors, spouses, and family members among participants of the Framingham Heart Study, making use of the fact that the participants had been asked to name their friends to facilitate follow-up in the study. The authors observed that when two persons perceived each other as friends, if one friend became obese during a given time interval, the other friend's chances of following suit increased by 171%. Among pairs of adult siblings, if one sibling became obese, the chance that the other would become obese increased by 40%. The results of this study also indicate that obesity is clustered in communities. For example, the risk that the friend of a friend of an obese person would be obese was about 20% higher in the observed network than in a random network; this effect vanished only by the fourth degree of separation.

In the past 7 years, our understanding of networks has undergone a revolution because of the emergence of a new array of theoretical tools and techniques for mapping out real networks. These advances have included some surprises indicating that most real networks in technological, social, and biologic systems have common designs that are governed by simple and quantifiable organizing principles.3 The growing interest in interconnectedness has brought into focus an often ignored issue: networks pervade all aspects of human health. One example of this trend involves social networks and their impact on the spread of obesity or pathogens — from influenza to the severe acute respiratory syndrome or the human immunodeficiency virus. The role of neural networks in various psychiatric and neurodegenerative diseases is another example. In fact, network analysis is poised to play the biggest role at the cellular level,4 since most cellular components are connected to each other through intricate regulatory, metabolic, and protein–protein interactions. Because of these many functional links, the defects of various genes spread throughout the intracellular network, affecting the activity of genes that otherwise carry no defects.

To understand various disease mechanisms, it is not sufficient to know the precise list of "disease genes"; instead, we should try to map out the detailed wiring diagram of the various cellular components that are influenced by these genes and gene products. Such network-based thinking has already provided insights into the pathogenesis of several diseases. For example, a recent study suggested that 18 of the 23 genes known to be associated with ataxia are part of a highly interlinked subnetwork5; in another example, a reverse-engineered subnetwork indicated that the androgen-receptor gene might be used to detect the aggressiveness of primary prostate cancer.6

The existence of intricate molecular links between subcellular components and disease genes raises another possibility: that is, diseases may not be as independent of each other as medical practitioners currently consider them to be. For example, could a genetic origin account for the fact that obesity is a risk factor for diabetes? A quick look at the list of genes associated with these two diseases indicates that several genes, including ectoenzyme nucleotide pyrophosphate phosphodiesterase (ENPP1), peroxisome-proliferator–activated receptor (PPAR), and — more recently — FTO, may be implicated in both diseases. In addition to the well-known link between diabetes and obesity, the large number of genes shared by often quite distinct disorders indicates that these diseases may have common genetic origins. Human diseases, therefore, themselves form a network in which two diseases are connected if they share at least one gene.7 In this disease network, obesity has links to seven diseases, including asthma, lipodystrophy, and glioblastoma (Figure 1). Thus, the network concept reveals a number of surprising connections between diseases, forcing us to rethink the way in which we classify and separate them
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