Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/97010
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dc.contributorDepartment of Health Technology and Informaticsen_US
dc.creatorZhang, Xen_US
dc.creatorXu, Hen_US
dc.creatorLi, GHen_US
dc.creatorLong, MTen_US
dc.creatorCheung, CLen_US
dc.creatorVasan, RSen_US
dc.creatorHsu, YHen_US
dc.creatorKiel, DPen_US
dc.creatorLiu, CTen_US
dc.date.accessioned2023-01-12T02:50:11Z-
dc.date.available2023-01-12T02:50:11Z-
dc.identifier.issn0884-0431en_US
dc.identifier.urihttp://hdl.handle.net/10397/97010-
dc.language.isoenen_US
dc.publisherWiley-Blackwell published for American Society for Bone and Mineral Researchen_US
dc.rights© 2021 American Society for Bone and Mineral Research (ASBMR)en_US
dc.rightsThis is the accepted version of the following article: Zhang, X., Xu, H., Li, G.H., Long, M.T., Cheung, C.-L., Vasan, R.S., Hsu, Y.-H., Kiel, D.P. and Liu, C.-T. (2021), Metabolomics Insights into Osteoporosis Through Association With Bone Mineral Density. J Bone Miner Res, 36: 729-738, which has been published in final form at https://doi.org/10.1002/jbmr.4240.en_US
dc.subjectDXAen_US
dc.subjectFracture Risk Assessment Metabolomicsen_US
dc.subjectGeneral Population Studiesen_US
dc.subjectOsteoporosisen_US
dc.titleMetabolomics insights into osteoporosis through association with bone mineral densityen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage729en_US
dc.identifier.epage738en_US
dc.identifier.volume36en_US
dc.identifier.issue4en_US
dc.identifier.doi10.1002/jbmr.4240en_US
dcterms.abstractOsteoporosis, a disease characterized by low bone mineral density (BMD), increases the risk for fractures. Conventional risk factors alone do not completely explain measured BMD or osteoporotic fracture risk. Metabolomics may provide additional information. We aim to identify BMD-associated metabolomic markers that are predictive of fracture risk. We assessed 209 plasma metabolites by liquid chromatography with tandem mass spectrometry (LC–MS/MS) in 1552 Framingham Offspring Study participants, and measured femoral neck (FN) and lumbar spine (LS) BMD 2 to 10 years later using dual-energy X-ray absorptiometry. We assessed osteoporotic fractures up to 27-year follow-up after metabolomic profiling. We identified 27 metabolites associated with FN-BMD or LS-BMD by LASSO regression with internal validation. Incorporating selected metabolites significantly improved the prediction and the classification of osteoporotic fracture risk beyond conventional risk factors (area under the curve [AUC] = 0.74 for the model with identified metabolites and risk factors versus AUC = 0.70 with risk factors alone, p = .001; net reclassification index = 0.07, p = .03). We replicated significant improvement in fracture prediction by incorporating selected metabolites in 634 participants from the Hong Kong Osteoporosis Study (HKOS). The glycine, serine, and threonine metabolism pathway (including four identified metabolites: creatine, dimethylglycine, glycine, and serine) was significantly enriched (false discovery rate [FDR] p value = .028). Furthermore, three causally related metabolites (glycine, phosphatidylcholine [PC], and triacylglycerol [TAG]) were negatively associated with FN-BMD, whereas PC and TAG were negatively associated with LS-BMD through Mendelian randomization analysis. In summary, metabolites associated with BMD are helpful in osteoporotic fracture risk prediction. Potential causal mechanisms explaining the three metabolites on BMD are worthy of further experimental validation. Our findings may provide novel insights into the pathogenesis of osteoporosis.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of bone and mineral research, Apr. 2021, v. 36, no. 4, p. 729-738en_US
dcterms.isPartOfJournal of bone and mineral researchen_US
dcterms.issued2021-04-
dc.identifier.isiWOS:000613825400001-
dc.identifier.pmid33434288-
dc.identifier.eissn1523-4681en_US
dc.description.validate202301 bcwhen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberHTI-0019-
dc.description.fundingSourceRGCen_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS46181787-
dc.description.oaCategoryGreen (AAM)en_US
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