Abstract
Objective
We examined anthropometric indicators to improve predictive ability of asymptomatic
vertebral fracture screening models.
Study design and setting
Data were obtained from the 1996 Japanese Population-based Osteoporosis (JPOS) Study.
McCloskey–Kanis criteria diagnosed vertebral deformities on X-ray absorptiometric
images in 693 women aged ≥50.The multiple logistic regression model included age,
height, weight, postmenopausal status, total hip BMD, and arm span (AS) or sitting
height as explanatory variables. Akaike's information criterion (AIC) evaluated model
goodness-of-fit.
Results
Age-adjusted AS and sitting height in subjects with and without vertebral deformities
were 147.2 ± 0.6 cm and 148.5 ± 0.2 cm (P = 0.055), 78.5 ± 0.5 cm and 79.9 ± 0.2 cm (P = 0.007), respectively. Every 5-cm increase in AS indicated 1.5-fold increased risk
of prevalent vertebral deformity in the model including age, height, weight, postmenopausal
status, and BMD. Including the explanatory variable AS in models yielded better predictive
accuracy than excluding AS (AIC, 441.7 vs 446.6, respectively). Sitting height did
not significantly influence model predictive ability.
Conclusion
Predictive accuracy of model for vertebral fracture including age, height, weight,
postmenopausal status, and BMD improved when AS was added as an explanatory variable.
Models to screen for asymptomatic vertebral fractures should include AS.
Keywords
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Article info
Publication history
Published online: September 18, 2009
Accepted:
September 3,
2009
Received:
July 22,
2009
Identification
Copyright
© 2009 Elsevier Ireland Ltd. Published by Elsevier Inc. All rights reserved.
