Autoimmunity: a decision theory model.

Concepts from statistical decision theory were used to analyse the detection problem faced by the body's immune system in mounting immune responses to bacteria of the normal body flora. Given that these bacteria are potentially harmful, that there can be extensive cross reaction between bacterial antigens and host tissues, and that the decisions are made in uncertainty, there is a finite chance of error in immune response leading to autoimmune disease. A model of ageing in the immune system is proposed that is based on random decay in components of the decision process, leading to a steep age dependent increase in the probability of error. The age incidence of those autoimmune diseases which peak in early and middle life can be explained as the resultant of two processes: an exponentially falling curve of incidence of first contact with common bacteria, and a rapidly rising error function. Epidemiological data on the variation of incidence with social class, sibship order, climate and culture can be used to predict the likely site of carriage and mode of spread of the causative bacteria. Furthermore, those autoimmune diseases precipitated by common viral respiratory tract infections might represent reactions to nasopharyngeal bacterial overgrowth, and this theory can be tested using monoclonal antibodies to search the bacterial isolates for cross reacting antigens. If this model is correct then prevention of autoimmune disease by early exposure to low doses of bacteria might be possible.