Characteristics of Gut Microbiota in Patients With Diabetes Determined by Data Mining Analysis of Terminal Restriction Fragment Length Polymorphisms

Yuta Nakamura, Yoshio Nagai, Toshio Kobayashi, Kentaro Furukawa, Yoichi Oikawa, Akira Shimada, Yasushi Tanaka

Abstract


Background: This study was performed to clarify whether gut microbiota obtained from fecal samples could identify the type of diabetes in patients of each gender by using a combination of terminal restriction fragment length polymorphism (T-RFLP) analysis and data mining.

Methods: A cross-sectional study was performed at three centers. Fecal samples were collected from 12 Japanese patients with type 1 diabetes mellitus (T1D), 18 patients with type 2 diabetes mellitus (T2D), and 31 subjects without diabetes mellitus (non-DM). Amplification of fecal 16S rRNA was carried out. After digestion of the amplification products with restriction enzymes (AluI, BslI, HaeIII, and MspI), terminal restriction fragments (T-RFs) of DNA were detected. A data mining algorithm (classification and regression tree (CART) modeling system) provides a decision tree that classifies subjects into various groups according to pre-assigned characteristics.

Results: Among men, the error rate was 2.4% with MspI, while error rates were 0.0% with other restriction enzymes. Among women, the error rate was 0.0% with all restriction enzymes. The operational taxonomic units (OTUs) incorporated into the decision tree differed between men and women.

Conclusions: We were able to classify the 16SrRNA gene amplification products obtained from fecal samples of T1D patients, T2D patients, and non-DM subjects with a high level of precision by combining T-RFLP analysis and data mining. Specific gut microbiota patterns were found for T1D and T2D patients, as well as a sex difference of the patterns.




J Clin Med Res. 2019;11(6):401-406
doi: https://doi.org/10.14740/jocmr3791


Keywords


Data mining; Diabetes; Gut microbiota; T-RFLP analysis

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