Ge Xin1, Liu Shong1, Liu Hui2. 1. College of Medical Laboratory, Dalian Medical University, Dalian 116044, China. 2. College of Medical Laboratory, Dalian Medical University, Dalian 116044, China. Electronic address: liuhui60@sina.com.
Abstract
OBJECTIVE: To assess the effect of genetic and non-genetic factors on indicators derived from waist circumference (WC) and body mass index (BMI) as well as inter-indicator differences in risk assessment age-related diseases including diabetes mellitus, coronary heart disease and liver cancer. METHODS: Height, weight and WC were measured in 100 families (students and their two parents), 41 subjects with regular physical exercise routines, and 170 patients with diabetes mellitus, coronary heart disease or liver cancer. The BMI, waist-height ratio (WHtR) and waist circumference density index (WCDI) were calculated for each subject. RESULTS: BMI was less affected by genetic factors, while WHtR and WCDI were greatly affected by genetic factors as revealed using multiple regression analysis. BMI, WHtR and WCDI were all sensitive to physical exercise according to ROC analysis; among these factors, the most sensitive indicator was WHtR. However, ROC analysis demonstrated that WCDI was more effective than BMI and WHtR for assessing the risk of three diseases. CONCLUSIONS: WCDI more accurately reflects the roles of both genetic and non-genetic factors, including aging, which can better predict disease.
OBJECTIVE: To assess the effect of genetic and non-genetic factors on indicators derived from waist circumference (WC) and body mass index (BMI) as well as inter-indicator differences in risk assessment age-related diseases including diabetes mellitus, coronary heart disease and liver cancer. METHODS: Height, weight and WC were measured in 100 families (students and their two parents), 41 subjects with regular physical exercise routines, and 170 patients with diabetes mellitus, coronary heart disease or liver cancer. The BMI, waist-height ratio (WHtR) and waist circumference density index (WCDI) were calculated for each subject. RESULTS: BMI was less affected by genetic factors, while WHtR and WCDI were greatly affected by genetic factors as revealed using multiple regression analysis. BMI, WHtR and WCDI were all sensitive to physical exercise according to ROC analysis; among these factors, the most sensitive indicator was WHtR. However, ROC analysis demonstrated that WCDI was more effective than BMI and WHtR for assessing the risk of three diseases. CONCLUSIONS: WCDI more accurately reflects the roles of both genetic and non-genetic factors, including aging, which can better predict disease.