Literature DB >> 33434288

Metabolomics Insights into Osteoporosis Through Association With Bone Mineral Density.

Xiaoyu Zhang1, Hanfei Xu1, Gloria Hy Li2, Michelle T Long3,4, Ching-Lung Cheung5, Ramachandran S Vasan4,6,7, Yi-Hsiang Hsu8,9,10, Douglas P Kiel8,9,10, Ching-Ti Liu1.   

Abstract

Osteoporosis, 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.
© 2021 American Society for Bone and Mineral Research (ASBMR). © 2021 American Society for Bone and Mineral Research (ASBMR).

Entities:  

Keywords:  DXA; FRACTURE RISK ASSESSMENT METABOLOMICS; GENERAL POPULATION STUDIES; OSTEOPOROSIS

Mesh:

Year:  2021        PMID: 33434288      PMCID: PMC8488880          DOI: 10.1002/jbmr.4240

Source DB:  PubMed          Journal:  J Bone Miner Res        ISSN: 0884-0431            Impact factor:   6.741


  36 in total

1.  Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.

Authors:  Michael J Pencina; Ralph B D'Agostino; Ralph B D'Agostino; Ramachandran S Vasan
Journal:  Stat Med       Date:  2008-01-30       Impact factor: 2.373

2.  Association between the metabolome and bone mineral density in pre- and post-menopausal Chinese women using GC-MS.

Authors:  Huanhuan Qi; Jun Bao; Guohua An; Gang Ouyang; Pengling Zhang; Chao Wang; Hanjie Ying; Pingkai Ouyang; Bo Ma; Qi Zhang
Journal:  Mol Biosyst       Date:  2016-06-21

3.  Web-based inference of biological patterns, functions and pathways from metabolomic data using MetaboAnalyst.

Authors:  Jianguo Xia; David S Wishart
Journal:  Nat Protoc       Date:  2011-05-05       Impact factor: 13.491

4.  Plasma phosphatidylcholine concentrations of polyunsaturated fatty acids are differentially associated with hip bone mineral density and hip fracture in older adults: the Framingham Osteoporosis Study.

Authors:  Emily K Farina; Douglas P Kiel; Ronenn Roubenoff; Ernst J Schaefer; L Adrienne Cupples; Katherine L Tucker
Journal:  J Bone Miner Res       Date:  2012-05       Impact factor: 6.741

Review 5.  Osteoporosis prevention, diagnosis, and therapy.

Authors: 
Journal:  JAMA       Date:  2001-02-14       Impact factor: 56.272

6.  FRAX and the assessment of fracture probability in men and women from the UK.

Authors:  J A Kanis; O Johnell; A Oden; H Johansson; E McCloskey
Journal:  Osteoporos Int       Date:  2008-02-22       Impact factor: 4.507

7.  Association between the metabolome and low bone mineral density in Taiwanese women determined by (1)H NMR spectroscopy.

Authors:  Ying-Shu You; Ching-Yu Lin; Hao-Jan Liang; Shen-Hung Lee; Keh-Sung Tsai; Jeng-Min Chiou; Yen-Ching Chen; Chwen-Keng Tsao; Jen-Hau Chen
Journal:  J Bone Miner Res       Date:  2014-01       Impact factor: 6.741

Review 8.  Diagnosis of osteoporosis and assessment of fracture risk.

Authors:  John A Kanis
Journal:  Lancet       Date:  2002-06-01       Impact factor: 79.321

9.  An investigation of coronary heart disease in families. The Framingham offspring study.

Authors:  W B Kannel; M Feinleib; P M McNamara; R J Garrison; W P Castelli
Journal:  Am J Epidemiol       Date:  1979-09       Impact factor: 4.897

10.  Mendelian randomization analysis with multiple genetic variants using summarized data.

Authors:  Stephen Burgess; Adam Butterworth; Simon G Thompson
Journal:  Genet Epidemiol       Date:  2013-09-20       Impact factor: 2.135

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1.  Vitamin D Deficiency in Women with Breast Cancer: A Correlation with Osteoporosis? A Machine Learning Approach with Multiple Factor Analysis.

Authors:  Alessandro de Sire; Luca Gallelli; Nicola Marotta; Lorenzo Lippi; Nicola Fusco; Dario Calafiore; Erika Cione; Lucia Muraca; Antonio Maconi; Giovambattista De Sarro; Antonio Ammendolia; Marco Invernizzi
Journal:  Nutrients       Date:  2022-04-11       Impact factor: 6.706

2.  Morusin induces osteogenic differentiation of bone marrow mesenchymal stem cells by canonical Wnt/β-catenin pathway and prevents bone loss in an ovariectomized rat model.

Authors:  Ming Chen; Hui Han; Siqi Zhou; Yinxian Wen; Liaobin Chen
Journal:  Stem Cell Res Ther       Date:  2021-03-12       Impact factor: 6.832

3.  Integrative lipidomic features identify plasma lipid signatures in chronic urticaria.

Authors:  Jie Li; Liqiao Li; Runqiu Liu; Lei Zhu; Bingjing Zhou; Yi Xiao; Guixue Hou; Liang Lin; Xiang Chen; Cong Peng
Journal:  Front Immunol       Date:  2022-07-28       Impact factor: 8.786

4.  Lipidomics Profiling of Patients with Low Bone Mineral Density (LBMD).

Authors:  Shereen M Aleidi; Mysoon M Al-Ansari; Eman A Alnehmi; Abeer K Malkawi; Ahmad Alodaib; Mohamed Alshaker; Hicham Benabdelkamel; Anas M Abdel Rahman
Journal:  Int J Mol Sci       Date:  2022-10-10       Impact factor: 6.208

Review 5.  Metabolomics in Bone Research.

Authors:  Jingzhi Fan; Vahid Jahed; Kristaps Klavins
Journal:  Metabolites       Date:  2021-07-01
  5 in total

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