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

Abstract

Background A variety of modifiable and non-modifiable 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 non-modifiable variables predictive of bone mineral content (BMC) and bone mineral density (BMD) in healthy U.S. 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 U.S. men and women ages 18–66 y, BMI 18–44 kg/m2 (n = 313) using non-modifiable anthropometric, physiological, and demographic variables, directly modifiable lifestyle (physical activity, tobacco use) and dietary (via food frequency questionnaire) variables, and variables approximating directly-modifiable behavior (circulating 25-hydroxycholecalciferol and stool pH). Results Machine learning models using non-modifiable variables explained more variation in BMC and BMD (highest R2 = 0.75) compared to when using only directly modifiable variables (highest R2 = 0.11). Machine learning models had better performance compared to multivariate linear regression, which had lower predictive value (highest R2 = 0.06) when using directly modifiable variables only. BMI, body fat %, 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. Conclusion Modifiable factors, such as diet, explained less variation in the data compared to non-modifiable factors, such as age, sex, and ethnicity in healthy U.S. men and women. Low stool pH predicted higher BMC and BMD. Clinical trial: registered on clinicaltrials.gov (Identifier: NCT02367287)

Association of Diet and Antimicrobial Resistance in Healthy U.S. Adults

“…Stool was homogenized in a Stomacher for 3 min and flash frozen on dry ice before being broken into aliquots and stored at 270°C until DNA isolation. Stool consistency was technician scored and pH analyzed as described previously (54,55). DNA extraction, library preparation, and sequencing.…”

“…The average intake of dietary components was calculated using the average across all at-home 24-h recalls that passed quality control, as we have described previously (71). Habitual diet was estimated using the 2014 Block FFQ (NutritionQuest, Berkeley, CA) and quality checked manually by a registered dietitian as described previously (55). Diet quality was estimated using the healthy eating index (HEI) (72).…”

“…The optimum model was fit to the entire training data set and then evaluated using the testing set. The predictive power of features for the model was determined using the SHapley Additive exPlanations (SHAP) package (93) as described previously (55). For reproducibility, the random state for each step of the pipeline was set to the same value.…”