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Updated: Jun 19, 2026

Precision Measurements and Parametric Models of Vertebral Endplates
Published on: September 17, 2019
Sex- and Age-Related Variation in Vertebral Geometry Influences Lumbar Spine Loading: An Image-Based Computational
Gregory G Knapik1, Ehud Mendel2, Eric Bourekas3
1Spine Research Institute, The Ohio State University, 210 Baker Systems Engineering, 1971 Neil Avenue, Columbus, OH, 43210, USA. knapik.1@osu.edu.
Purpose:
Low back disorders are highly prevalent and disproportionately affect women and older adults, yet the biomechanical mechanisms underlying these demographic disparities remain unclear. This study aimed to investigate how sex- and age-related anatomical differences in vertebral geometry influence lumbar spine loading patterns.
Methods:
Three hundred sixty lumbar motion segments were derived from CT scans of 60 asymptomatic adults (30 males, 30 females; ages 20-69, evenly distributed by decade). Subject-specific multibody dynamic models were developed for each segment featuring anatomically accurate vertebrae and facet joints with simplified discs and loading conditions to isolate geometric effects. Segments were tested under seven physiologic load cases, and disc and facet measures were examined to assess the effects of sex and age on spinal loading.
Results:
Age significantly influenced facet contact forces and the magnitude of intervertebral disc loading with older subjects exhibiting increased facet forces and elevated shear loads but reduced compression. Sex-based differences were pronounced when loads were normalized by endplate cross-sectional areas. Females exhibited up to 31% higher normalized compression and shear loads than males. Significant asymmetry and inter-subject variability were also observed, particularly in the younger and older age groups.
Conclusion:
Sex- and age-related anatomical variation in vertebral geometry significantly impacts lumbar spine biomechanics. These findings highlight the need for more inclusive and anatomically realistic modeling approaches that reflect the diversity of the population for accurate risk assessment and personalized clinical strategies.
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