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首页> 外文期刊>BMC Medical Informatics and Decision Making >Combinational risk factors of metabolic syndrome identified by fuzzy neural network analysis of health-check data
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Combinational risk factors of metabolic syndrome identified by fuzzy neural network analysis of health-check data

机译：通过健康检查数据的模糊神经网络分析确定代谢综合征的组合危险因素

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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.
机译：背景技术以代谢综合症为代表的与生活方式有关的疾病是复杂相互作用的结果。通过使用长期随访中收集的两项大型研究的健康检查数据，我们搜索了与代谢综合征发展相关的危险因素。方法在我们的原始研究中，我们从1803名日本男性雇员中选择了77名在随访过程中发生代谢综合征的病例受试者和152名无生活方式相关风险成分的健康对照受试者。在复制研究中，我们从31343名其他日本男性雇员中选择了2196名病例受试者和2196名健康对照受试者。通过使用模糊神经网络（FNN）的生物信息学方法，我们搜索了与MetS相关的任何重要组合。为了确保通过FNN分析选择的风险组合在统计上是可靠的，我们进行了包括调整在内的逻辑回归分析。结果我们选择了升高水平的γ-谷氨酰转肽酶（γ-GTP）和升高的白细胞（WBC）数量作为代谢综合征发展风险因素的最重要组合。 FNN在复制研究中也发现了相同的趋势。即使进行了调整，γ-GTP水平和WBC计数的临床特征仍具有统计学意义，证实了从模糊神经网络获得的结果是合理的。相关比表明，γ-GTP水平升高与习惯性饮酒有关，而白细胞计数高与习惯性吸烟有关。结论通过对来自大型长期研究的健康检查数据进行模糊神经网络分析获得的结果可用于提供涉及γ-GTP水平和WBC计数的个性化新型诊断和治疗方法。

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