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Purpose Studies have reported the influence of adolescent obesity on development of adult diabetes, but the effect of the growth pattern during this period has rarely been explored. Also, the tri-ponderal mass index (TMI) was thought to be a better estimation of adolescent body fat levels than the body mass index (BMI), so we sought to investigate whether growth trajectories derived by these two indices could predict incident diabetes. Methods We conducted a study by using the Taipei City Hospital Radiation Building Database, a longitudinal cohort established from 1996 until now. Physical exam results including blood test results were collected annually and the BMI z-score/TMI growth trajectory groups during 13–18 years of age were identified using growth mixture modeling. A Cox proportional hazard model for incident diabetes was used to examine the risk of baseline obese status and different BMI/TMI growth trajectories. Results Five growth trajectory groups were identified for the BMI z-score and the TMI. During approximately 20,400 person-years follow-up, 33 of 1,387 participants developed diabetes. Baseline obesity defined by the BMI z-score and the TMI were both related to adult diabetes. The persistent increase TMI growth trajectory exhibited a significantly increased risk of diabetes after adjusting for baseline obese status and other correlated covariates (hazard ratio: 2.85, 95% confidence interval (CI): 1.01–8.09). There was no association between BMI growth trajectory groups and incident diabetes. Conclusions A specific TMI growth trajectory pattern during adolescence might be critical for diabetes prevention efforts.

Health examinations can obtain relatively complete health information and thus are important for the personal and public health management. For clinicians, one of the most important works in the health examinations is to interpret the health examination results. Continuously interpreting numerous health examination results of healthcare receivers is tedious and error-prone. This paper proposes a clinical decision support system to assist solving above problems. In order to customize the clinical decision support system intuitively and flexibly, this paper also proposes the rule syntax to implement computer-interpretable logic for health examinations. It is our purpose in this paper to describe the methodology of the proposed clinical decision support system. The evaluation was performed by the implementation and execution of decision rules on health examination results and a survey on clinical decision support system users. It reveals the efficiency and user satisfaction of proposed clinical decision support system. Positive impact of clinical data interpretation is also noted.

Long-term, low-dose rate radiation exposure early in life might cause subsequent psychological stress and an increased risk of depression decades later.