The Value of Historical Height Loss for Detecting Vertebral Fractures in Postmenopausal Women in China

ABSTRACT

Objectives

The diagnosis and management of osteoporosis and osteoporotic fractures are challenging in rural and underdeveloped areas of China because medical resources are inaccessible; thus, a simple and accurate method is essential for the detection of vertebral fractures. We aimed to examine the relationship between historical height loss (HHL) and vertebral fractures in postmenopausal Chinese women.

Material and Methods

A cross-sectional study of 255 postmenopausal women aged 50 years or older was conducted in September 2017. Demographic data, including self-reported tallest historical height and current height were analyzed. Vertebral fractures were assessed using X-ray radiography and HHL thresholds were examined using specificity and sensitivity testing.

Results

The average age of the 255 participants was 66.3 ± 9.0 years and their mean HHL was 3.5 ± 2.8 cm. The 24 women who were found to have vertebral fractures were older, had more years since menopause (YSM), and a larger HHL compared to those without vertebral fractures. Logistic regression analysis showed that age was a better predictor of vertebral fractures than HHL was, and the cutoff age for detecting vertebral fractures was 71 years, with an area under the receiver operating characteristic curve of 0.750.

Conclusions

Although the women in this study with vertebral fractures had a greater height loss than those without fractures, it was apparent that age, rather than HHL, is the best way to determine who is most likely to develop vertebral fractures.

KEYWORDS:

• Historical height loss
• osteoporosis
• vertebral fracture
• postmenopausal women
• China

Acknowledgments

We want to thank all of the participants who voluntarily consented to disclose information for this study.

Disclosure Statement

The authors report no conflicts of interest.

Additional information

Funding

This work was supported by National Nature Science Foundation of China under grant [number 81471091, 81870622] and [Nature Science Foundation of Hunan Province] under grant [number 2018JJ2574].