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

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

doi:10.1186/1472-6947-12-80

Abstract

BackgroundLifestyle-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.MethodsIn 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.ResultsWe 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.ConclusionsThis 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.

Highlights

Lifestyle-related diseases represented by metabolic syndrome develop as results of complex interaction
The weight, Body-mass index (BMI), systolic blood pressure, diastolic blood pressure, serum total cholesterol, serum triglyceride, and fasting plasma glucose were significantly higher in the case subjects than in the healthy control subjects, whereas serum high-density lipoprotein (HDL)–cholesterol was significantly lower in the case subjects
fuzzy neural network (FNN) analysis By means of the FNN analysis of health check-up data before study start from the original study (Table 4), we identified a combination of the γ-glutamyltranspeptidase (γ-GTP) level and the white blood cell (WBC) count as being indicative of Metabolic syndrome (MetS)

Summary

Introduction

Lifestyle-related diseases represented by metabolic syndrome develop as results of complex interaction. To investigate the relationship between diet or physical activity and risk marker plays effective roles in finding the most suitable lifestyle factor to improve developing MetS. It is useful for proposing a personalized diagnostic and therapeutic method. There is an urgent need to establish an appropriate and sensitive screening marker to identify individuals at a high risk of developing MetS, thereby preventing a further increase in its incidence Indices such as the low-density lipoprotein (LDL) to high-density lipoprotein (HDL) ratio (L/H) [3] or the ratio of adiponectin to homeostasis model assessment–insulin resistance (adiponectin/HOMA-IR ratio) [4] have been proposed as combinational risk factors. There is, a need to identify new combinational risk factors

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