Physiological and Anthropometric Factors Associated With Spine Loading Estimates From Imaging‐Based Subject‐Specific Musculoskeletal Models
ABSTRACT Background 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 aime...
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| Language: | English |
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Wiley
2025-06-01
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| Series: | JOR Spine |
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| Online Access: | https://doi.org/10.1002/jsp2.70059 |
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| author | Brett T. Allaire Fjola Johannesdottir Mary L. Bouxsein Dennis E. Anderson |
| author_facet | Brett T. Allaire Fjola Johannesdottir Mary L. Bouxsein Dennis E. Anderson |
| author_sort | Brett T. Allaire |
| collection | DOAJ |
| description | ABSTRACT Background 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. Methods 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. Results 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. Conclusions 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. |
| format | Article |
| id | doaj-art-86db4de9068f4fa8a4d7ea0ea733055c |
| institution | OA Journals |
| issn | 2572-1143 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | JOR Spine |
| spelling | doaj-art-86db4de9068f4fa8a4d7ea0ea733055c2025-08-20T02:22:02ZengWileyJOR Spine2572-11432025-06-0182n/an/a10.1002/jsp2.70059Physiological and Anthropometric Factors Associated With Spine Loading Estimates From Imaging‐Based Subject‐Specific Musculoskeletal ModelsBrett T. Allaire0Fjola Johannesdottir1Mary L. Bouxsein2Dennis E. Anderson3Center for Advanced Orthopaedic Studies, Beth Israel Deaconess Medical Center Boston Massachusetts USACenter for Advanced Orthopaedic Studies, Beth Israel Deaconess Medical Center Boston Massachusetts USACenter for Advanced Orthopaedic Studies, Beth Israel Deaconess Medical Center Boston Massachusetts USACenter for Advanced Orthopaedic Studies, Beth Israel Deaconess Medical Center Boston Massachusetts USAABSTRACT Background 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. Methods 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. Results 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. Conclusions 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.https://doi.org/10.1002/jsp2.70059computed tomographymusculoskeletal modelspredictive analysisprincipal component analysisspine loading |
| spellingShingle | Brett T. Allaire Fjola Johannesdottir Mary L. Bouxsein Dennis E. Anderson Physiological and Anthropometric Factors Associated With Spine Loading Estimates From Imaging‐Based Subject‐Specific Musculoskeletal Models JOR Spine computed tomography musculoskeletal models predictive analysis principal component analysis spine loading |
| title | Physiological and Anthropometric Factors Associated With Spine Loading Estimates From Imaging‐Based Subject‐Specific Musculoskeletal Models |
| title_full | Physiological and Anthropometric Factors Associated With Spine Loading Estimates From Imaging‐Based Subject‐Specific Musculoskeletal Models |
| title_fullStr | Physiological and Anthropometric Factors Associated With Spine Loading Estimates From Imaging‐Based Subject‐Specific Musculoskeletal Models |
| title_full_unstemmed | Physiological and Anthropometric Factors Associated With Spine Loading Estimates From Imaging‐Based Subject‐Specific Musculoskeletal Models |
| title_short | Physiological and Anthropometric Factors Associated With Spine Loading Estimates From Imaging‐Based Subject‐Specific Musculoskeletal Models |
| title_sort | physiological and anthropometric factors associated with spine loading estimates from imaging based subject specific musculoskeletal models |
| topic | computed tomography musculoskeletal models predictive analysis principal component analysis spine loading |
| url | https://doi.org/10.1002/jsp2.70059 |
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