Substantiating the effect of DXA variables in the prediction of diabetes mellitus using machine learning

Type 2 Diabetes Mellitus(DM) is a chronic condition that impairs the way the body processes blood sugar(glucose). Over 10 percent of the US population is known to be affected by Type 2 DM as of 2018, and almost quarter of them are unaware or undiagnosed, thus, making early detection and treatment of diabetes an important step in mitigating the associated health risks. Previous studies show that waist circumference and waist-height ratio are found to be better indicators of diabetes than BMI. These measures of central obesity create great interest to study different measures of body fat distribution. The main objective of this study is to provide evidence to prove that variables derived from the DXA(Dual-energy X-ray absorptiometry) analysis including regional fat distribution profiles are better indicators of DM when compared to conventional metrics such as Body Mass Index(BMI). A multi-class classification is performed using Random Forest Classifier to classify patients as ‘Normal’, ‘Prediabetic’ or ‘Diabetic’ across various subsets of data obtained from the NHANES diabetes cohort. Feature selection techniques such as ANOVA/Chi-square, Recursive Feature Eliminations(RFE) and intrinsic feature importance scores from the classifier were used to filter the most important features. It was observed that fat distribution features from DXA can be used as a viable alternative to conventional metrics in the detection of DM. Notably, head fat percentage was proven to be a prominent feature to identify DM. Thus, our study demonstrates the potential of fat distribution variables as a potential standalone or surrogate biomarker for Type 2 DM.