Machine Learning Identifies Stool pH as a Predictor of Bone Mineral Density in Healthy Multiethnic US Adults (original) (raw)

Background A variety of modifiable and nonmodifiable factors such as ethnicity, age, and diet have been shown to influence bone health. Previous studies are usually limited to analyses focused on the association of a few a priori variables or on a specific subset of the population. Objective Dietary, physiological, and lifestyle data were used to identify directly modifiable and nonmodifiable variables predictive of bone mineral content (BMC) and bone mineral density (BMD) in healthy US men and women using machine-learning models. Methods Ridge, lasso, elastic net, and random forest models were used to predict whole-body, femoral neck, and spine BMC and BMD in healthy US men and women ages 18–66 y, with a BMI (kg/m2) of 18–44 (n = 313), using nonmodifiable anthropometric, physiological, and demographic variables; directly modifiable lifestyle (physical activity, tobacco use) and dietary (via FFQ) variables; and variables approximating directly modifiable behavior (circulating 25-hyd...