Identification of Heavy Smokers through Their Intestinal Microbiota by Data Mining Analysis

Abstract

The intestinal microbiota compositions of 92 Japanese men were identified following consumption of identical meals for 3 days, and collected feces were analyzed through terminal restriction fragment length polymorphism. The obtained operational taxonomic units and smoking habits of subjects were analyzed by a data mining software. The constructed decision tree was able to identify explicitly the groups of smokers and nonsmokers. In particular, 4 smokers, who smoked 20 cigarettes/day, i.e., heavy smokers, were gathered in the same group of the decision tree and were clearly identified. Related operational taxonomic unit were traced to understand the species of bacteria, but all were found to be uncultured bacteria.