This study aims to provide the relevant basis upon which prediction of dyslipidemia should be made based on body information. Using the National Health Insurance big data (3,312,971 people) canonical correlation analysis was performed between body information and lipid-profile. Body information included age, height, weight and waist circumference, while the lipid-profile included total cholesterol, triglycerides, HDL cholesterol and LDL cholesterol. As a result, when the waist circumference and the weight are large, triglycerides increase and HDL cholesterol level decreases. In terms of age, weight, waist circumference, and HDL cholesterol, the canonical variates (the degree of influence) were significantly different according to sex. In particular, the canonical variate was dramatically changed around the forties and fifties in women in terms of weight, waist circumference, and HDL cholesterol. The canonical correlation results of the health care big data presented in this study will help construct a predictive model that can evaluate an individual's health status based on body information that can be easily measured in a non-invasive manner.