The development of an integrated and personalized healthcare system is becoming an important issue in the modern healthcare industry. One of main objectives of integrated healthcare system is to effectively manage patients having chronic diseases that require long term care and its temporal information plays an important role to manage the statuses of diseases. Thus, a patient having chronic disease needs to visit the hospital periodically, which generates large volume of medical examination data. Among the various chronic diseases, metabolic syndrome (MS) has become a popular chronic disease in many countries. There have been efforts to develop an MS risk quantification and prediction model and to integrate it into personalized healthcare system, so as to predict the risk of having MS in the future. However, the development of methods for temporal progress management of metabolic syndrome has not been widely investigated. This paper proposes a method for identifying the temporal progress of MS patients' status based on the chronological clustering methodology. To investigate the temporal changes of disease status, we develop a chronological distance variance model that quantifies the difference of areal similarity degree (ASD) values between estimated and examined results of MS risk factors. We evaluate the clinical effectiveness of the temporal progress model by using sample subjects' examination results that have been measured for 10 years. We further elaborate the accuracy of the proposed temporal progress estimation method by using multiple linear regression method. Then, we develop a tier-based patients' MS status classification based on the chronological distance variance. The tier classification is based on the sensitivity for temporal change of MS status according to different values of control range of chronological distance variance. Our proposed temporal change identification method and patients' tier classification are expected to be incorporated with the integrated healthcare systems to help physicians with identifying the temporal progress of MS patients' health status and MS patients with self-management at home environments.