Allaire Brett T, Johannesdottir Fjola, Bouxsein Mary L, Anderson Dennis E
Center for Advanced Orthopaedic Studies, Beth Israel Deaconess Medical Center Boston Massachusetts USA.
Department of Orthopaedic Surgery Harvard Medical School Boston Massachusetts USA.
JOR Spine. 2025 Apr 11;8(2):e70059. doi: 10.1002/jsp2.70059. eCollection 2025 Jun.
Subject-specific musculoskeletal models may be used to estimate spine loads that cannot be measured in vivo. Model generation methods may use detailed measurements extracted from medical imaging, but it may be possible to create accurate models without these measurements. We aimed to determine which physiological and anthropometric factors are associated with spine loading and should be accounted for in model creation.
We created models of 440 subjects from the Framingham Heart Study Multi-detector CT Study, extracting muscle morphology and spine profile information from CT scans of the trunk. Five lifting activities were simulated, and compressive and shear loading estimates were produced. We performed principal component analysis on the loading data from three locations in the spine, as well as univariate correlations between predictor variables and each principal component (PC). We identified multivariate predictive regression models for each PC and individual loading estimate.
A single PC explained 90% of the variability in compressive loading, while four PCs were identified that explained 10%-37% individually, 86% in total, of the variability in shear loading. Univariate analysis showed that body weight, BMI, lean mass, and waist circumference were most associated with the compression PC and first shear PC. Multivariate regression modeling showed predictor variables predicted 94% of the variability in the compression PC, but only 54% in the first shear PC, with body weight having the highest contribution. Additional shear PCs were less predictable. Level- and activity-specific compressive loading was predicted using a limited set of physiological and anthropometric factors.
This work identifies easily measured characteristics, particularly weight and height, along with sex, associated with subject-specific loading estimates. It suggests that compressive loading, or models to evaluate compressive loading, may be based on a limited set of anthropometric attributes. Shear loading appears more complex and may require additional information not captured in the set of factors we examined.
特定个体的肌肉骨骼模型可用于估计体内无法测量的脊柱负荷。模型生成方法可能会使用从医学成像中提取的详细测量数据，但也有可能在没有这些测量数据的情况下创建准确的模型。我们旨在确定哪些生理和人体测量因素与脊柱负荷相关，以及在模型创建中应予以考虑。
我们根据弗雷明汉心脏研究多排CT研究创建了440名受试者的模型，从躯干的CT扫描中提取肌肉形态和脊柱轮廓信息。模拟了五种举重活动，并生成了压缩和剪切负荷估计值。我们对来自脊柱三个位置的负荷数据进行了主成分分析，以及预测变量与每个主成分（PC）之间的单变量相关性分析。我们为每个PC和个体负荷估计值确定了多变量预测回归模型。
单个PC解释了压缩负荷变异性的90%，而确定了四个PC，它们分别解释了剪切负荷变异性的10%-37%，总共解释了86%。单变量分析表明，体重、BMI、瘦体重和腰围与压缩PC和第一个剪切PC最相关。多变量回归建模表明，预测变量预测了压缩PC中94%的变异性，但在第一个剪切PC中仅预测了54%，其中体重的贡献最大。其他剪切PC的可预测性较低。使用一组有限的生理和人体测量因素预测了特定水平和活动的压缩负荷。
这项工作确定了易于测量的特征，特别是体重和身高，以及与特定个体负荷估计相关的性别。这表明压缩负荷或评估压缩负荷的模型可能基于一组有限的人体测量属性。剪切负荷似乎更为复杂，可能需要我们所研究的因素集中未涵盖的其他信息。
