- Title
- Food groups and risk of forearm fractures in postmenopausal women in Chengdu, China
- Creator
- Xu, L.; Dibley, M.; D'Este, C.; Phillips, M.; Porteous, J.; Attia, J.
- Relation
- Climacteric Vol. 12, Issue 3, p. 222-229
- Publisher Link
- http://dx.doi.org/10.1080/13697130802626958
- Publisher
- Informa Healthcare
- Resource Type
- journal article
- Date
- 2009
- Description
- Forearm fractures are a major cause of disability in postmenopausal women. However, no prior report on the relationship of dietary patterns and forearm fracture in mainland China is available and the results from Western studies may not be applicable to the Chinese population. To determine whether food groups are associated with the risk of forearm fracture among postmenopausal women in China. The design case is a control study. Cases were 209 postmenopausal women aged 50–70 years with new forearm fractures, identified in hospitals in Chengdu. Controls were age- and district-matched subjects randomly selected from the community of Chengdu. Dietary habits and food group intake were measured by a structured food frequency questionnaire. The dietary pattern in Chinese postmenopausal women was of high intakes of cereals, vegetables, fruits, beans or bean products, but lower intakes of meat and milk. The estimated odds ratios (95% confidence intervals) for forearm fracture were 0.53 (0.42–0.67) for each quintile increase in vegetable intake, 0.73 (0.59–0.92) for each quintile increase in cereal intake, and 0.26 (0.14–0.48) in subjects who met World Health Organization (WHO) dietary recommendations for vegetables and fruits. This is the first report indicating that the levels of vegetable and cereal intake, and that meeting WHO recommendations for vegetable and fruit intake, are associated with a decrease in the risk of forearm fractures in Chinese postmenopausal women.
- Subject
- dietary pattern; forearm fracture; risk factor; women; postmenopausal; China
- Identifier
- http://hdl.handle.net/1959.13/917192
- Identifier
- uon:8227
- Identifier
- ISSN:1369-7137
- Language
- eng
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