Ensrud KE, Crandall CJ (2017) Osteoporosis. Ann Intern Med 167:ITC7. https://doi.org/10.7326/AITC201708010
Atik OS, Gunal I, Korkusuz F (2006) Burden of Osteoporosis. Clin Orthop Relat Res 443:19–24. https://doi.org/10.1097/01.blo.0000200248.34876.fe
Siris ES, Miller PD, Barrett-Connor E, et al. Identification and fracture outcomes of undiagnosed low bone mineral density in postmenopausal women. 8
Vestergaard P, Rejnmark L, Mosekilde L (2005) Osteoporosis is markedly underdiagnosed: a nationwide study from Denmark. Osteoporos Int 16:134–141. https://doi.org/10.1007/s00198-004-1680-8
Haseltine KN, Chukir T, Smith PJ et al (2021) Bone mineral density: clinical relevance and quantitative assessment. J Nucl Med 62:446–454. https://doi.org/10.2967/jnumed.120.256180
Article CAS PubMed PubMed Central Google Scholar
Cherian KE, Kapoor N, Meeta M, Paul TV (2021) Screening tools for osteoporosis in india: where do we place them in current clinical care? J Midlife Health 12:257–262. https://doi.org/10.4103/jmh.jmh_216_21
Garnero P (2017) The utility of biomarkers in osteoporosis management. Mol Diagn Ther 21:401–418. https://doi.org/10.1007/s40291-017-0272-1
Article CAS PubMed Google Scholar
Patti GJ, Yanes O, Siuzdak G (2012) Metabolomics: the apogee of the omic triology. Nat Rev Mol Cell Biol 13:263–269. https://doi.org/10.1038/nrm3314
Article CAS PubMed PubMed Central Google Scholar
Panahi N, Arjmand B, Ostovar A et al (2021) Metabolomic biomarkers of low BMD: a systematic review. Osteoporos Int 32:2407–2431. https://doi.org/10.1007/s00198-021-06037-8
Article CAS PubMed Google Scholar
Lv H, Jiang F, Guan D et al (2016) Metabolomics and Its application in the development of discovering biomarkers for osteoporosis research. IJMS 17:2018. https://doi.org/10.3390/ijms17122018
Article CAS PubMed PubMed Central Google Scholar
Guijas C, Montenegro-Burke JR, Warth B et al (2018) Metabolomics activity screening for identifying metabolites that modulate phenotype. Nat Biotechnol 36:316–320. https://doi.org/10.1038/nbt.4101
Article CAS PubMed PubMed Central Google Scholar
Roth HE, Powers R (2022) Meta-analysis reveals both the promises and the challenges of clinical metabolomics. Cancers 14:3992. https://doi.org/10.3390/cancers14163992
Article CAS PubMed PubMed Central Google Scholar
Didelez V, Sheehan N (2007) Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res 16:309–330. https://doi.org/10.1177/0962280206077743
Brion M-JA, Benyamin B, Visscher PM, Smith GD (2014) Beyond the single SNP: emerging developments in mendelian randomization in the “Omics” era. Curr Epidemiol Rep 1:228–236. https://doi.org/10.1007/s40471-014-0024-2
Boehm FJ, Zhou X (2022) Statistical methods for Mendelian randomization in genome-wide association studies: a review. Comput Struct Biotechnol J 20:2338–2351. https://doi.org/10.1016/j.csbj.2022.05.015
Article CAS PubMed PubMed Central Google Scholar
The Multiple Tissue Human Expression Resource (MuTHER) Consortium, Shin S-Y, Fauman EB et al (2014) An atlas of genetic influences on human blood metabolites. Nat Genet 46:543–550. https://doi.org/10.1038/ng.2982
Medina-Gomez C, Kemp JP, Trajanoska K et al (2018) Life-course genome-wide association study meta-analysis of total body BMD and assessment of age-specific effects. Am J Hum Genet 102(1):88–102
Article CAS PubMed PubMed Central Google Scholar
Zheng H-F, Forgetta V, Hsu Y-H et al (2015) Whole-genome sequencing identifies EN1 as a determinant of bone density and fracture. Nature 526:112–117. https://doi.org/10.1038/nature14878
Article CAS PubMed PubMed Central Google Scholar
Kemp JP, Morris JA, Medina-Gomez C et al (2017) Identification of 153 new loci associated with heel bone mineral density and functional involvement of GPC6 in osteoporosis. Nat Genet 49:1468–1475. https://doi.org/10.1038/ng.3949
Article CAS PubMed PubMed Central Google Scholar
Surakka I, Fritsche LG, Zhou W et al (2020) MEPE loss-of-function variant associates with decreased bone mineral density and increased fracture risk. Nat Commun 11:4093. https://doi.org/10.1038/s41467-020-17315-0
Article CAS PubMed PubMed Central Google Scholar
Davies NM, Holmes MV, Davey Smith G (2018) Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ k601. https://doi.org/10.1136/bmj.k601
Hemani G, Zheng J, Elsworth B et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. Elife 7:e34408. https://doi.org/10.7554/eLife.34408s
Article PubMed PubMed Central Google Scholar
Pierce BL, Ahsan H, VanderWeele TJ (2011) Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol 40:740–752. https://doi.org/10.1093/ije/dyq151
Burgess S, Butterworth A, Thompson SG (2013) Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 37:658–665. https://doi.org/10.1002/gepi.21758
Article PubMed PubMed Central Google Scholar
Burgess S, Thompson SG (2017) Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol 32:377–389. https://doi.org/10.1007/s10654-017-0255-x
Article PubMed PubMed Central Google Scholar
Bowden J, Davey Smith G, Haycock PC, Burgess S (2016) Consistent estimation in mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 40:304–314. https://doi.org/10.1002/gepi.21965
Article PubMed PubMed Central Google Scholar
Shardell M, Ferrucci L (2016) Instrumental variable analysis of multiplicative models with potentially invalid instruments. Statist Med 35:5430–5447. https://doi.org/10.1002/sim.7069
Thompson SG, Sharp SJ (1999) Explaining heterogeneity in meta-analysis: a comparison of methods. Stat Med 18:2693–2708. https://doi.org/10.1002/(sici)1097-0258(19991030)18:20%3c2693::aid-sim235%3e3.0.co;2-v
Article CAS PubMed Google Scholar
Verbanck M, Chen C-Y, Neale B, Do R (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50:693–698. https://doi.org/10.1038/s41588-018-0099-7
Article CAS PubMed PubMed Central Google Scholar
Wishart DS, Guo A, Oler E et al (2022) HMDB 5.0: the human metabolome database for 2022. Nucleic Acids Res 50:D622–D631. https://doi.org/10.1093/nar/gkab1062
Article CAS PubMed Google Scholar
Pang Z, Zhou G, Ewald J et al (2022) Using MetaboAnalyst 5.0 for LC-HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data. Nat Protoc 17:1735–1761. https://doi.org/10.1038/s41596-022-00710-w
Article CAS PubMed Google Scholar
Jewison T, Su Y, Disfany FM et al (2014) SMPDB 2.0: big improvements to the small molecule pathway database. Nucleic Acids Res 42:D478-484. https://doi.org/10.1093/nar/gkt1067
Article CAS PubMed Google Scholar
Kanehisa M, Goto S, Sato Y et al (2012) KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40:D109-114. https://doi.org/10.1093/nar/gkr988
Article CAS PubMed Google Scholar
Bulik-Sullivan B, Finucane HK, Anttila V et al (2015) An atlas of genetic correlations across human diseases and traits. Nat Genet 47:1236–1241. https://doi.org/10.1038/ng.3406
Article CAS PubMed PubMed Central Google Scholar
Giambartolomei C, Vukcevic D, Schadt EE et al (2014) Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet 10:e1004383. https://doi.org/10.1371/journal.pgen.1004383
Article CAS PubMed PubMed Central Google Scholar
Pruim RJ, Welch RP, Sanna S et al (2010) LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics 26:2336–2337. https://doi.org/10.1093/bioinformatics/btq419
Article CAS PubMed PubMed Central Google Scholar
Liu L, Wen Y, Zhang L et al (2018) Assessing the associations of blood metabolites with osteoporosis: a mendelian randomization study. J Clin Endocrinol Metab 103:1850–1855. https://doi.org/10.1210/jc.2017-01719
Article PubMed PubMed Central Google Scholar
Moayyeri A, Cheung C-L, Tan KC et al (2018) Metabolomic pathways to osteoporosis in middle-aged women: a genome-metabolome-wide mendelian randomization study. J Bone Miner Res 33:643–650. https://doi.org/10.1002/jbmr.3358
留言 (0)