We assessed the associations between the APOA5  −1131T>C polymorphism and lipid parameters and other risk factors of the metabolic syndrome in Korean subjects. A total of 2,901 participants from 20 oriental medical hospitals in Korea were enrolled between 2006 and 2011. According to the modified National Cholesterol Education Program Adult Treatment Panel III definitions, subjects were classified into the metabolic syndrome group and control group. The APOA5  −1131T>C genotype was significantly associated with serum high-density lipoprotein cholesterol levels (effect = − 1.700 mg/dL, P=6.550-E07) in the total study population after adjustment for differences in age and gender. The association of the APOA5  −1131T>C genotype with serum log-transformed triglyceride was also significant in an additive genetic model (effect = 0.056 mg/dL, P=2.286E-19). After adjustment for age and gender, we determined that the odds ratio for the occurrence of the metabolic syndrome was 1.322 for C-allele carriers in the additive model (95% CI = [1.165 − 1.501], P=1.48E-05). In the current study, we demonstrated that the APOA5  −1131T>C polymorphism is associated with the metabolic syndrome because of its remarkable effect on serum triglyceride levels in Korean subjects.

The voice has been used to classify the four constitution types, and to recognize a subject's health condition by extracting meaningful physical quantities, in traditional Korean medicine. In this paper, we propose a method of selecting the reliable variables from various voice features, such as frequency derivative features, frequency band ratios, and intensity, from vowels and a sentence. Further, we suggest a process to extract independent variables by eliminating explanatory variables and reducing their correlation and remove outlying data to enable reliable discriminant analysis. Moreover, the suitable division of data for analysis, according to the gender and age of subjects, is discussed. Finally, the vocal features are applied to a discriminant analysis to classify each constitution type. This method of voice classification can be widely used in the u-Healthcare system of personalized medicine and for improving diagnostic accuracy.