Proteomics and machine learning in the prediction and explanation of low pectoralis muscle area - Nicholas A. Enzer; Joe Chiles; Stefanie Mason; Toru Shirahata; Victor Castro; Elizabeth Regan; Bina Choi; Nancy F. Yuan; Alejandro A. Diaz; George R. Washko; Merry-Lynn McDonald; Raúl San José Estépar; et al. (Scientific Reports, 2024)
Abstract Low muscle mass is associated with numerous adverse outcomes independent of other associated comorbid diseases. We aimed to predict and understand an individual’s risk for developing low muscle mass using proteomics and machine learning. We identified eight biomarkers associated with low pectoralis muscle area (PMA). We built three random forest classification models that used either clinical measures, feature selected biomarkers, or both to predict development of low PMA. The area under the receiver operating characteristic curve for each model was: clinical-only = 0.646, biomarker-only = 0.740, and combined = 0.744. We displayed the heterogenetic nature of an individual’s risk for developing low PMA and identified two distinct subtypes of participants who developed low PMA. While additional validation is required, our methods for identifying and understanding individual and group risk for low muscle mass could be used to enable developments in the personalized prevention of low muscle mass.