Association between the metabolome and bone mineral density in pre- and post-menopausal Chinese women using GC-MS. Issue 7 (11th May 2016)

• Record Type: Journal Article
• Authors: Qi, Huanhuan; Bao, Jun; An, Guohua; Ouyang, Gang; Zhang, Pengling; Wang, Chao; Ying, Hanjie; Ouyang, Pingkai; Ma, Bo; Zhang, Qi
• Is Part Of: Molecular bioSystems. Volume 12:Issue 7(2016:Jul.)
• Page Start: 2265
• Page End: 2275
• Publication Date: 2016-05-11
• Subjects: Molecular biology -- Periodicals; Biochemistry -- Periodicals; 571.7405
• DOI: 10.1039/c6mb00181e
• Languages: English
• ISSNs: 1742-206X

Abstract

Abstract : The present study describes for the first time, a metabolic profile reflecting the osteoporosis progression in 364 pre- and postmenopausal Chinese women using GC-MS. Abstract : The present study describes for the first time, a metabolic profile reflecting the osteoporosis progression in 364 pre- and postmenopausal Chinese women using GC-MS. In order to accurately evaluate the dynamic changes of metabolites along with estrogen deficiency and osteoporosis progression, we divided these subjects into the following four groups: premenopausal women with normal bone mass density (BMD, group I), postmenopausal women with normal BMD (group II), postmenopausal women with osteopenia (group III) and postmenopausal women with osteoporosis (group IV), according to their menopause or low BMD status. Principal component analysis (PCA) and Partial least squares-discriminant analysis (PLS-DA) were used to evaluate the associations of metabolic changes with low BMD or estrogen deficiency. Twelve metabolites identified by the PLS-DA model were found to be able to differentiate low BMD groups from normal BMD groups. Of the 12 metabolites, five free fatty acids (LA, oleic acid, AA and 11, 14-eicosadienoic acid) have the most potential to be used as osteoporosis biomarkers due to their better correlations with BMD, and high sensitivity and specificity in distinguishing the low BMD groups from the normal BMD groups calculated by the receiver operating characteristic curve (ROC). The lipid profile may be useful for osteoporosis prediction and diagnosis.