Over the next few years, Christakis and Fowler managed a team that painstakingly sifted through the records. When they were done, they had a map of how 5,124 subjects were connected, tracing a web of 53,228 ties between friends and family and work colleagues. Next they analyzed the data, beginning with tracking patterns of how and when Framingham residents became obese. Soon they had created an animated diagram of the entire social network, with each resident represented on their computer screens as a dot that grew bigger or smaller as he or she gained or lost weight over 32 years, from 1971 to 2003. When they ran the animation, they could see that obesity broke out in clusters. People weren’t just getting fatter randomly. Groups of people would become obese together, while other groupings would remain slender or even lose weight.
And the social effect appeared to be quite powerful. When a Framingham resident became obese, his or her friends were 57 percent more likely to become obese, too. Even more astonishing to Christakis and Fowler was the fact that the effect didn’t stop there. In fact, it appeared to skip links. A Framingham resident was roughly 20 percent more likely to become obese if the friend of a friend became obese — even if the connecting friend didn’t put on a single pound. Indeed, a person’s risk of obesity went up about 10 percent even if a friend of a friend of a friend gained weight.
