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

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/m(2)) 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-hydroxycholecalciferol and stool pH). Results: Machine-learning models using nonmodifiable variables explained more variation in BMC and BMD (highest R-2 = 0.75) compared with when using only directly modifiable variables (highest R-2 = 0.11). Machine-learning models had better performance compared with multivariate linear regression, which had lower predictive value (highest R-2 = 0.06) when using directly modifiable variables only. BMI, body fat percentage, height, and menstruation history were predictors of BMC and BMD. For directly modifiable features, betaine, cholesterol, hydroxyproline, menaquinone-4, dihydrophylloquinone, eggs, cheese, cured meat, refined grains, fruit juice, and alcohol consumption were predictors of BMC and BMD. Low stool pH, a proxy for fermentable fiber intake, was also predictive of higher BMC and BMD. Conclusions: Modifiable factors, such as diet, explained less variation in the data compared with nonmodifiable factors, such as age, sex, and ethnicity, in healthy US men and women. Low stool pH predicted higher BMC and BMD. This trial was registered at www.clinicaltrials.gov as NCT02367287.

Automated summary

This study found that nonmodifiable factors, such as age, sex, and ethnicity, have a greater impact on bone health in healthy US men and women compared to directly modifiable factors like diet. Additionally, low stool pH was predictive of higher bone mineral content and density.