Enzer Nicholas A, Chiles Joe, Mason Stefanie, Shirahata Toru, Castro Victor, Regan Elizabeth, Choi Bina, Yuan Nancy F, Diaz Alejandro A, Washko George R, McDonald Merry-Lynn, Estépar Raul San José, Ash Samuel Y
Brigham and Women's Hospital.
University of Alabama at Birmingham.
Res Sq. 2024 Mar 4:rs.3.rs-3957125. doi: 10.21203/rs.3.rs-3957125/v1.
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 8 biomarkers associated with low pectoralis muscle area (PMA). We built 3 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 2 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.
低肌肉量与众多不良后果相关,且独立于其他相关合并症。我们旨在利用蛋白质组学和机器学习预测并了解个体发生低肌肉量的风险。我们鉴定出8种与低胸肌面积(PMA)相关的生物标志物。我们构建了3个随机森林分类模型,分别使用临床指标、特征选择的生物标志物或两者结合来预测低PMA的发生。每个模型的受试者工作特征曲线下面积分别为:仅临床指标 = 0.646,仅生物标志物 = 0.740,两者结合 = 0.744。我们展示了个体发生低PMA风险的异质性,并鉴定出发生低PMA的2种不同参与者亚型。虽然需要进一步验证,但我们识别和理解个体及群体低肌肉量风险的方法可用于推动低肌肉量个性化预防的发展。