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Faculty & Staff Honors

Faculty & Staff Honors

UAB Post-doctoral Fellow Awarded Grant-in-Aid from St. Luke’s International University

Dr. Keisuke Ejima, post-doctoral fellow in the office of energetics at the University of Alabama at Birmingham, is the recipient of a Grant-in-Aid for Epidemiological Research (1,000,000 Japanese Yen in amount) from the Center of Clinical Epidemiology, St. Luke’s International University, which was established to “cultivate the active clinical research mind among health professionals to improve medical practice.”

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[Photo: Dr. Keisuke Ejima]

The grant-in-aid will fund a study titled “Comparing Different Types of Obesity Index Using Proportional Hazards Model: What Would Be the Most Excellent Obesity Index?” While body mass index (BMI) has been used as a guide for treating and screening excess adiposity, its limitation in the clinical-setting as the only screening tool for obesity treatment is widely accepted. To address this inadequacy recently, clinically relevant obesity staging systems have been used. The main goal of Dr. Ejima’s study is to compare two obesity staging systems in terms of their predictive accuracy or discriminative ability for mortality as well as for other outcomes such as disability. This research will be conducted at UAB under the mentorship of Dr. David B. Allison, distinguished professor and director of the Nutrition Obesity Research Center (NORC) and office of energetics, and in collaboration with Dr. Tapan Mehta (co-mentor), assistant professor in the department of health services administration and associate scientist of the NORC.

Dr. Ejima completed his doctoral degree in Information Science and Technology from the University of Tokyo in March 2014. He joined UAB’s NORC and office of energetics in September 2014. His research interest is to understand and solve the problems in obesity epidemiology using mathematical model. His post-doctoral research will focus on quantifying mother-to-child obesity inheritability (both genetic and non-genetic) and its potential impact on the obesity epidemic, and on constructing statistical models for optimization of obesity treatment in the clinical-setting.