In our study, we used an FNN as a computational method to analyze complex characteristics. The FNN analysis is a powerful machine-learning method for detecting, with maximal accuracy, significant combinations of characteristics that are associated with a particular attribute. By using an FNN, we identified that a combination of the γ-GTP level and the WBC count is a characteristic that is associated with MetS. As shown in Table, the accuracy in FNN analysis was similar to the accuracy in multiple linear regression and the accuracy in multiple logistic regression. However, FNN analysis also has an ability to visualize the risk of the high γ-GTP level and high WBC count group easily using fuzzy rule as Figure. The FNN also lacked statistical significance, so we reexamined selected characteristics by means of statistical analysis with suitable adjustments. The statistical results confirmed that the γ-GTP level and the WBC count are significant factors in MetS, confirming that the FNN has good predictive powers and is suitable for use in practical applications.
We excluded the characteristics included in judgments of metabolic syndrome. We firstly conducted FNN analysis including the components of metabolic syndrome, selecting a combination of triglycerides and WBC. However, we thought these components directly affect the prevalence of MetS. We aimed to search latent risk factors using remaining 16 clinical and laboratory characteristics. We showed that an elevated γ-GTP level and an elevated WBC count are combinational risk factors for MetS. γ-GTP is a marker of fatty liver disease and γ-GTP levels have been found to be associated with the prevalence of MetS in previous East Asian studies [20
]. An increase in levels of liver enzymes may be related to excess deposition of fat in the liver. The WBC count is a marker of systemic inflammation and it has also been found to be associated with the prevalence of MetS in a previous study [23
]. The WBC count is controlled by cytokines, especially interleukin-6 and interleukin-8 [24
], and WBCs play a major role in inflammatory processes and in defending the body against infectious disorders. In addition, a previous study has shown that the mean WBC count increases with an increase in serum γ-GTP [25
]; this implied that elevated γ-GTP levels might reflect subclinical inflammation. This result from our FNN method may show that this combination has a synergistic effect.
Our study also showed that habitual drinking is related to an elevated level of γ-GTP, in agreement with a previous study [26
]. However, we also showed that there is no significant association between habitual drinking and MetS. Similarly, we showed that habitual smoking is linked to an increase in the WBC count. However, the association between habitual smoking and MetS was low significant. This tendency was the same that found in a previous study [27
]. Although γ-GTP levels and WBC counts are generally included in blood tests performed during periodic health examinations, these characteristics have seldom been considered in risk assessment. Our result could be useful in a personalized risk-prevention method, advising people with elevated γ-GTP level and an elevated WBC count to improve their diet and physical activity.
In this study, completely healthy people were used as controls. We also conducted logistic regression analysis between case subjects who developed MetS during the follow-up and normal control subjects who weren’t MetS during the follow-up. In original study, including 77 case subjects and 597 normal control subjects, the significant association between γ-GTP and MetS wasn’t observed (P
0.213). On the other hand, the significant association between WBC and MetS was observed (P
). In replication study, including 2196 case subjects and 27246 normal control subjects, the significant association between γ-GTP and MetS was observed (P
). The significant association between WBC and MetS was also observed (P
). Although our study couldn’t find significant association between γ-GTP and MetS in original study partly because of small sample, we showed significant associations in replication study consisted of large subjects. This result suggests that both of the association between γ-GTP and MetS and the association between WBC and MetS may be derived from the difference between those who will develop the overt clinical picture of metabolic syndrome and those who will develop some of its components without fulfilling the criteria for its diagnosis.
Our present study has several limitations however. First, for clinical data before the follow-up, we used a modified definition of MetS involving the BMI instead of the waist circumference; however, several studies have shown that the BMI is an equally effective characteristic as the waist circumference and it has been adopted for analyses of the association between the adiponection gene and metabolic traits, including MetS [28
]. Secondly, we analyzed data from male subjects exclusively. This was because only one woman showed an indication of MetS among 2061 Japanese company employees in our original study. Although the potential bias was minimized by adjusting for age, drinking habit, and smoking habit, our findings may have limited value in the case of women. Thirdly, we could not subdivide drinking habit and smoking habit quantitatively. As in a previous study [26
], the strength of risk of MetS may be related to the drinking status or the smoking status.