Spinal aBMD only explains 50–80% of vertebral strength, and the application of aBMD measurements in isolation cannot accurately identify individuals who are likely to eventually experience bone fracture, due to the low sensitivity of the test. For appropriate treatment intervention, a more sensitive test of bone strength is needed. Such a test should include not only bone mineral density, but also bone quality. Quantitative computed tomography-based finite element methods (QCT/FEM) may allow structural analyses taking these factors into consideration to accurately predict bone strength (PBS). To date, however, basic data have not been reported regarding the prediction of bone strength by QCT/FEM with reference to age in a normal population. The purpose of this study was thus to create a database on PBS in a normal population as a preliminary trial. With these data, parameters that affect PBS were also analyzed. Participants in this study comprised individuals who participated in a health checkup program with CT at our hospital in 2009. Participants included 217 men and 120 women (age range, 40–89 years). Exclusion criteria were provided. Scan data of the second lumber vertebra (L2) were isolated and taken from overall CT data for each participant obtained with simultaneous scans of a calibration phantom containing hydroxyapatite rods. A FE model was constructed from the isolated data using Mechanical Finder software. For each of the FE models, A uniaxial compressive load with a uniform distribution and uniform load increment was applied. For each participant, height and weight were measured, BMI was calculated. Simple linear regression analysis was used to estimate correlations between age and PBS as analyzed by QCT/FEM. Changes in PBS with age were also evaluated by grouping participants into 5-year age brackets. One-way analysis of variance was used to compare average PBS for participants in each age range. Mean PBS in the 40–44 year age range was taken as the young adult mean (YAM). The ratio of mean PBS in each age group to YAM was calculated as a percentage. A multivariate statistical technique was used to determine how PBS was affected by age, height, weight, and BMI.Introduction
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