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J Clin Med Res. 2019 Jun;11(6):401-406. doi: 10.14740/jocmr3791. Epub 2019 May 10.

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

Author information

1
Division of Metabolism and Endocrinology, Department of Internal Medicine, St. Marianna University School of Medicine, 2-16-1, Sugao, Miyamae-ku, Kawasaki, Kanagawa 216-8511, Japan.
2
Miyagi University, Sendai, Miyagi 982-0215, Japan.
3
Furukawa Hospital, Yokohama, Kanagawa 221-0021, Japan.
4
Department of Internal Medicine, Tokyo Saiseikai Central Hospital, Tokyo 108-0073, Japan.
5
Department of Endocrinology and Diabetes, Saitama Medical University, Iruma-gun, Saitama 350-0495, Japan.

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.

KEYWORDS:

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

Conflict of interest statement

The authors declare no conflict of interest.

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