Zhenwei Zhou, a PhD student studying biostatistics, developed a statistical method to examine the relationship between metabolic syndrome and inflammatory biomarkers that vary with BMI and insulin resistance.
Zhenwei Zhou, a PhD student in the Department of Biostatistics at the School of Public Health, has received a Distinguished Student Paper Award from the International Biometric Society Eastern North American Region (ENAR).
Zhou presented his paper, “Modeling Metabolic Syndrome and Inflammatory Biomarkers via Bayesian Graphical Regression with Multiple Data Types,” at the ENAR 2021 Spring Meeting, which was held online March 14-17.
Zhou’s paper, co-authored by Ching-Ti Liu, professor of biostatistics, describes an innovative statistical method and analyzes the relationships between metabolic syndrome, certain inflammatory biomarkers, and demographic variables, and how those relationships vary with other factors, such as body mass index (BMI) and insulin resistance status.
The research is significant for two reasons, says Liu. First, Zhou’s analysis uncovered some interesting relationships between metabolic syndrome and several important inflammatory biomarkers that vary with BMI. These findings add to researchers’ understanding of metabolic syndrome (a cluster of conditions that increase a person’s risk of heart disease, stroke, and type 2 diabetes) and may someday assist doctors in making more personalized health management plans. Second, the statistical method Zhou developed could be very useful in analyzing other datasets that involve multiple data types.
The new statistical method is a variation of a Bayesian graphical regression method, says Zhou.
“One restriction of that method is that the variables in the model need to be continuous and normally distributed,” he says. Zhou wanted a method that could handle not only continuous variables, but also binary and ordinal variables. So he found a way to modify Bayesian graphical regression to accommodate this wider range of data types.
Zhou says he was surprised and honored to win the ENAR award. The judges’ validation of his work has boosted his confidence and motivated him to continue pursuing research in related fields, he says.
“I am grateful that Professor Liu has been providing me advice and support,” Zhou says. “He has helped me realize that, when you are developing biostatistical methods, it is very useful to start by performing analysis on a real-world problem. Sometimes you will find that the current available methods are not ideal, and then you can work on developing new methods that are better at meeting the needs of medical research.”