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1.  Combinational risk factors of metabolic syndrome identified by fuzzy neural network analysis of health-check data 
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.
doi:10.1186/1472-6947-12-80
PMCID: PMC3469424  PMID: 22853735
Data mining; Combinational risk factor; Fuzzy neural network; Glutamyltranspeptidase; Lifestyle disease; Personalized diagnostic method; White blood cell
2.  Relation of a common variant of the adiponectin gene to serum adiponectin concentration and metabolic traits in an aged Japanese population 
Adiponectin is an adipocyte-derived protein that is down-regulated in obesity-linked disorders. Variants of the adiponectin gene (ADIPOQ) have been shown to affect adiponectin level. We have now examined the relation of polymorphisms of ADIPOQ to adiponectin concentration and to metabolic disorders in the Kita-Nagoya Genomic Epidemiology study, a population-based study of elderly Japanese. The genomic region including ADIPOQ was genotyped for 30 single nucleotide polymorphisms in 500 subjects of a screening population with the use of a fluorescence- or colorimetry-based allele-specific DNA primer–probe assay system. Four polymorphisms were then selected for genotyping in an additional 2797 subjects. Serum adiponectin level was negatively associated with metabolic abnormalities after adjustment for age and sex. The minor alleles of the rs1656930, Ile164Thr, and rs9882205 polymorphisms were associated with a low serum adiponectin level. Whereas the minor alleles of rs1656930 and rs9882205 were common (minor allele frequency of 6.2 and 38.5%, respectively), that of Ile164Thr was rare (0.9%). The minor allele of rs1656930 was positively associated with systolic blood pressure and the prevalence of hypertension. The association of rs1656930 with adiponectin level was replicated in an independent population. A subject with the 164Thr/Thr genotype had an extremely low serum adiponectin level (0.6 μg/ml) and the phenotype of metabolic syndrome. Our results suggest that a common variant of ADIPOQ, the minor allele of rs1656930, is associated with hypoadiponectinemia and hypertension. Screening for a common genetic background underlying low adiponectin levels might provide important information for assessment and management of metabolic disorders.
doi:10.1038/ejhg.2010.201
PMCID: PMC3062002  PMID: 21150884
adiponectin; polymorphism; metabolic disorder; hypertension; epidemiology

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