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Open Access Research article

Combinational risk factors of metabolic syndrome identified by fuzzy neural network analysis of health-check data

Yasunori Ushida1, Ryuji Kato1, Kosuke Niwa2, Daisuke Tanimura3, Hideo Izawa3, Kenji Yasui45, Tomokazu Takase2, Yasuko Yoshida26, Mitsuo Kawase2, Tsutomu Yoshida7, Toyoaki Murohara3 and Hiroyuki Honda16*

Author Affiliations

1 School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan

2 NGK Insulators, Ltd, Sudacho, Mizuho-ku, Nagoya, 467-8530, Japan

3 Nagoya University School of Medicine, Tsurumaicho, Showa-ku, Nagoya, 466-8550, Japan

4 NGK Health Insurance Society, Sudacho, Mizuho-ku, Nagoya, 467-8530, Japan

5 Aoyama Clinic, Sakae 3-7-13, Naka-ku, Nagoya, 460-0008, Japan

6 MEXT Innovative Research Center for Preventative Medical Engineering, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan

7 Faculty of Pharmacy, Meijo University, Yagotoyama 150, Tenpaku-ku, Nagoya, 468-8503, Japan

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BMC Medical Informatics and Decision Making 2012, 12:80  doi:10.1186/1472-6947-12-80

Published: 1 August 2012

Abstract

Background

Lifestyle-related diseases represented by metabolic syndrome develop as results of complex interaction. By using health check-up data from two large studies collected during a long-term follow-up, we searched for risk factors associated with the development of metabolic syndrome.

Methods

In our original study, we selected 77 case subjects who developed metabolic syndrome during the follow-up and 152 healthy control subjects who were free of lifestyle-related risk components from among 1803 Japanese male employees. In a replication study, we selected 2196 case subjects and 2196 healthy control subjects from among 31343 other Japanese male employees. By means of a bioinformatics approach using a fuzzy neural network (FNN), we searched any significant combinations that are associated with MetS. To ensure that the risk combination selected by FNN analysis was statistically reliable, we performed logistic regression analysis including adjustment.

Results

We selected a combination of an elevated level of γ-glutamyltranspeptidase (γ-GTP) and an elevated white blood cell (WBC) count as the most significant combination of risk factors for the development of metabolic syndrome. The FNN also identified the same tendency in a replication study. The clinical characteristics of γ-GTP level and WBC count were statistically significant even after adjustment, confirming that the results obtained from the fuzzy neural network are reasonable. Correlation ratio showed that an elevated level of γ-GTP is associated with habitual drinking of alcohol and a high WBC count is associated with habitual smoking.

Conclusions

This result obtained by fuzzy neural network analysis of health check-up data from large long-term studies can be useful in providing a personalized novel diagnostic and therapeutic method involving the γ-GTP level and the WBC count.

Keywords:
Data mining; Combinational risk factor; Fuzzy neural network; Glutamyltranspeptidase; Lifestyle disease; Personalized diagnostic method; White blood cell