PDF(Pubmed)
Abstract：
Sarcopenic obesity (SO) is characterized by concomitant sarcopenia and obesity and presents a high risk of disability, morbidity, and mortality among older adults. However, predictions based on sequential neural network SO studies and the relationship between physical fitness factors and SO are lacking. This study aimed to develop a predictive model for SO in older adults by focusing on physical fitness factors. A comprehensive dataset of older Korean adults participating in national fitness programs was analyzed using sequential neural networks. Appendicular skeletal muscle/body weight was defined as SO using an anthropometric equation. Independent variables included body fat (BF, %), waist circumference, systolic and diastolic blood pressure, and various physical fitness factors. The dependent variable was a binary outcome (possible SO vs normal). We analyzed hyperparameter tuning and stratified K-fold validation to optimize a predictive model. The prevalence of SO was significantly higher in women (13.81%) than in men, highlighting sex-specific differences. The optimized neural network model and Shapley Additive Explanations analysis demonstrated a high validation accuracy of 93.1%, with BF% and absolute grip strength emerging as the most influential predictors of SO. This study presents a highly accurate predictive model for SO in older adults, emphasizing the critical roles of BF% and absolute grip strength. We identified BF, absolute grip strength, and sit-and-reach as key SO predictors. Our findings underscore the sex-specific nature of SO and the importance of physical fitness factors in its prediction.
摘要：
肌肉减少性肥胖（SO）的特征是伴随的肌肉减少症和肥胖，并存在残疾的高风险，发病率，和老年人的死亡率。然而,缺乏基于序贯神经网络SO研究的预测以及体能因子与SO之间的关系。本研究旨在通过关注身体健康因素来开发老年人SO的预测模型。使用顺序神经网络分析了参加全民健身计划的韩国老年人的综合数据集。使用人体测量方程将阑尾骨骼肌/体重定义为SO。独立变量包括身体脂肪(BF，%),腰围,收缩压和舒张压,和各种体能因素。因变量是二元结果(可能的SO与正常)。我们分析了超参数调整和分层K倍验证，以优化预测模型。女性的SO患病率(13.81%)明显高于男性,突出性别差异。优化后的神经网络模型和Shapley加法解释分析表明，验证准确率高达93.1%，BF%和绝对握力成为SO最有影响力的预测因子。这项研究为老年人的SO提供了一个高度准确的预测模型，强调BF%和绝对握力的关键作用。我们确认了BF,绝对握力，作为关键的SO预测因子。我们的发现强调了SO的性别特异性以及身体健康因素在预测中的重要性。
