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.

Peptide array-based analysis of the specific IgE and IgG4 in cow's milk allergens and its use in allergy evaluation.

Cow's milk (CM) is one of the major causes of food allergies in children. We constructed a peptide array consisting of a linear 16-mer peptide library with an offset of 3-mer, which corresponds to the primary sequences of six major CM allergens. The immune reactivity to cow's milk proteins diminishes with age and clinical tolerance commonly occurs. Although the central role of IgE in allergy is well established, the role of other specific antibody classes in obtaining immunotolerance is not well known. The hypothesis that patients become tolerant when they develop immunological changes particularly with the IgG4 isotype has been proposed. In this study, the binding pattern of the CM protein-specific IgE and IgG4 epitopes was measured using the peptide array with sera of 12 patients with persistent CM allergy (CMA), sera of 5 children who outgrew CMA, and sera of 7 CM-sensitized children without allergy symptoms. In CMA patients the IgG4/IgE fluorescence intensity ratios varied greatly from peptide to peptide, and the scatter plots of IgE versus IgG4 signals using significant IgE-binding peptides showed different distribution patterns. When setting the boundary line based on the IgG4/IgE ratio (IgG4/IgE=2), patients with persistent CMA and CM-sensitized children can be distinguished by the plot pattern of peptides. Furthermore, the number of peptide plots in these regions was less in children who outgrew CMA. The approach employed in this study will allow for the distinction between CMA and CM-sensitization, and will enable the estimation of CMA outgrow by monitoring the time elapsed data.

Development of peptide arrays for detection of IgE-binding epitopes in cow's milk allergens.

Peptide arrays have become versatile tools for high throughput screening assays in biomedical and pharmaceutical research. In this study, we constructed a peptide array that contained linear peptide fragments reported as IgE-binding epitopes for cow's milk allergy (CMA). Various peptides with different solubility in aqueous solutions were dissolved in the buffer solutions containing sodium dodecyl sulfate, and we achieved a consistent spotting of peptide solutions using a piezoelectric ceramic micropump. The IgE-binding patterns were successfully detected by observing the binding of Alexa 647-labeled anti-human IgE using sera from CMA patients. Our technique in this study will provide a potent capability for the development of a peptide array for mapping IgE-epitopes in milk proteins, and it will help researchers better understand the IgE-epitopes associated with the clinical outcome of CMA.