Applying Data Mining to Classify Age by Intestinal Microbiota in 92 Healthy Men Using a Combination of Several Restriction Enzymes for T-RFLP Experiments

The composition of the intestinal microbiota was measured following consumption of identical meals for 3 days in 92 Japanese men, and terminal restriction fragment length polymorphism (T-RFLP) was used to analyze their feces. The obtained operational taxonomic units (OTUs) and the subjects’ ages were classified by using Data mining (DM) software that compared these data with continuous data and for 5 partitions for age divided at 5 years intervals between the ages of 30 and 50. The DM provided Decision trees in which the selected OTUs were closely related to the ages of the subjects. DM was also used to compare the OTUs from the T-RFLP data with seven restriction enzymes (two enzymes of 516f-BslI and 516f-HaeIII, two enzymes of 27f-MspI and 27f-AluI, three enzymes of 35f-HhaI, 35f-MspI and 35f-AluI) and their various combinations. The OTUs delivered from the five enzyme-digested partitions were analyzed to classify their age clusters. For use in future DM processing, we discussed the enzymes that were effective for accurate classification. We selected two OTUs (HA624 and HA995) that were useful for classifying the subject’s ages. Depending on the 16S rRNA sequences of the OTUs, Ruminicoccus obeum clones 1-4 were present in 18 of 36 bacterial candidates in the older age group-related OTU (HA624). On the other hand, Ruminicoccus obeum clones 1-33 were present in 65 of 269 candidates in the younger age group-related OUT (HA995).