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

Yasunori Ushida, Ryuji Kato, Kosuke Niwa, Daisuke Tanimura, Hideo Izawa, Kenji Yasui, Tomokazu Takase, Yasuko Yoshida, Mitsuo Kawase, Tsutomu Yoshida, Toyoaki Murohara, Hiroyuki Honda

Research output: Contribution to journalArticle

10 Citations (Scopus)

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 -glutamyltranspeptidase (-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 -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 -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 -GTP level and the WBC count.

Original languageEnglish
Article number80
JournalBMC Medical Informatics and Decision Making
Volume12
Issue number1
DOIs
Publication statusPublished - 2012

Keywords

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

ASJC Scopus subject areas

  • Health Policy
  • Health Informatics

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    Ushida, Y., Kato, R., Niwa, K., Tanimura, D., Izawa, H., Yasui, K., Takase, T., Yoshida, Y., Kawase, M., Yoshida, T., Murohara, T., & Honda, H. (2012). Combinational risk factors of metabolic syndrome identified by fuzzy neural network analysis of health-check data. BMC Medical Informatics and Decision Making, 12(1), [80]. https://doi.org/10.1186/1472-6947-12-80