Health is a multidimensional and continual concept. Traditional latent analytic approaches have inherent deficits in capturing the complex nature of the concept; however, the Grade of Membership (GoM) model is well suited for this problem. We applied the GoM method to a set of 31 indicators to construct ideal profiles of health status based on physical, mental and social support items among 848 adult twins from Qingdao, China. Four profiles were identified: healthy individuals (pure type I), individuals with personality disorders (pure type II), individuals with mental impairments (pure type III) and individuals with physical impairments (pure type IV). The most frequently occurring combination in this population was profiles I, II, IV (14.74%), followed by profiles I, II, III, IV (13.44%), and then type I (11.08%). Only 13.56% of subjects fell completely into one single pure type, most individuals exhibited some of the characteristics of two or more pure types. Our results indicated that, compared to conventional statistical methods, the GoM model was more suited to capture the complex concept of health, reflecting its multidimensionality and continuity, while also exhibiting preferable reliability. This study also made an important contribution to research on GoM application in non-independent samples.