Estimating the Joint and Independent Effects of Body Mass Index and Age on Diabetic Mellitus Using Log-Linear Model

Abstract

Aims: This study aims to investigate the relationship between body mass index, age, and the prevalence of diabetes mellitus using a log-linear model, exploring potential interaction effects between these variables.
Methods: This study used a log-linear model to analyze the relationship between BMI, age, and diabetes in 768 females from the Akimel O’odham Indians Diabetes Dataset. The analysis considered BMI-age interactions and used goodness-of-fit statistics to select the most insightful model, focusing on identifying significant interactions and estimating the effects of BMI and age on diabetes prevalence.
Results: Both BMI and age are significantly associated with diabetes prevalence. Higher BMI and older age correlate with increased diabetes likelihood. The interaction between age and BMI reveals that BMI’s impact on diabetes risk is more pronounced in older individuals. These findings highlight the importance of considering both factors when assessing diabetes risk.
Conclusions: BMI and age significantly influence diabetes prevalence in females, with their interaction adding complexity to risk assessment. This effect is more pronounced in older individuals. Further research is needed to understand the underlying mechanisms and develop targeted interventions. This study contributes to informing public health strategies for diabetes prevention and management.