Deciphering the environmental chemical basis of muscle quality decline by interpretable machine learning models.

Background: Sarcopenia is known as a decline in skeletal muscle quality and function that is associated with age. Sarcopenia is linked to diverse health problems, including endocrine-related diseases. Environmental chemicals (ECs), a broad class of chemicals released from industry, may influence muscle quality decline.

Objectives: In this work, we aimed to simultaneously elucidate the associations between muscle quality decline and diverse EC exposures based on the data from the 2011-2012 and 2013-2014 survey cycles in the National Health and Nutrition Examination Survey (NHANES) project using machine learning models.

Methods: Six machine learning models were trained based on the EC and non-EC exposures from NHANES to distinguish low from normal muscle quality index status. Different machine learning metrics were evaluated for these models. The Shapley additive explanations (SHAP) approach was used to provide explainability for machine learning models.

Results: Random forest (RF) performed best on the independent testing data set. Based on the testing data set, ECs can independently predict the binary muscle quality status with good performance by RF (area under the receiver operating characteristic curve = 0.793; area under the precision-recall curve = 0.808). The SHAP ranked the importance of ECs for the RF model. As a result, several metals and chemicals in urine, including 3-phenoxybenzoic acid and cobalt, were more associated with the muscle quality decline.

Conclusions: Altogether, our analyses suggest that ECs can independently predict muscle quality decline with a good performance by RF, and the SHAP-identified ECs can be closely related to muscle quality decline and sarcopenia. Our analyses may provide valuable insights into ECs that may be the important basis of sarcopenia and endocrine-related diseases in United States populations.