title: Combinational risk factors of metabolic syndrome identified by fuzzy neural network analysis of health-check data.
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.
authors.affiliation: School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan.
authors.forename: Yasunori
authors.initials: Y
authors.lastname: Ushida
authors.forename: Ryuji
authors.initials: R
authors.lastname: Kato
authors.forename: Kosuke
authors.initials: K
authors.lastname: Niwa
authors.forename: Daisuke
authors.initials: D
authors.lastname: Tanimura
authors.forename: Hideo
authors.initials: H
authors.lastname: Izawa
authors.forename: Kenji
authors.initials: K
authors.lastname: Yasui
authors.forename: Tomokazu
authors.initials: T
authors.lastname: Takase
authors.forename: Yasuko
authors.initials: Y
authors.lastname: Yoshida
authors.forename: Mitsuo
authors.initials: M
authors.lastname: Kawase
authors.forename: Tsutomu
authors.initials: T
authors.lastname: Yoshida
authors.forename: Toyoaki
authors.initials: T
authors.lastname: Murohara
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Honda
mesh_terms: D000328:Adult; D015992:Body Mass Index; D019295:Computational Biology / Q000379:methods* / Q000592:standards; D003955:Diagnostic Tests, Routine; D004651:Employment; D004781:Environmental Exposure; D005500:Follow-Up Studies; D017143:Fuzzy Logic; D006801:Humans; D007564:Japan; D007958:Leukocyte Count; D008019:Life Style; D016015:Logistic Models; D008297:Male; D024821:Metabolic Syndrome / Q000097:blood / Q000175:diagnosis*; D016571:Neural Networks, Computer; D010043:Outcome and Process Assessment, Health Care; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D013048:Specimen Handling; D011795:Surveys and Questionnaires; D005723:gamma-Glutamyltransferase / Q000097:blood / Q000378:metabolism
chemical_list: D005723:gamma-Glutamyltransferase