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Metabolomics of Osteoporosis in Humans: A Systematic Review

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Abstract

Purpose of Review

To systematically review recent studies investigating the association between metabolites and bone mineral density (BMD) in humans.

Methods

Using predefined keywords, we searched literature published from Jan 1, 2019 to Feb 20, 2022 in PubMed, Web of Science, Embase, and Scopus. Studies that met the predefined exclusion criteria were excluded. Among the included studies, we identified metabolites that were reported to be associated with BMD by at least three independent studies.

Recent Findings

A total of 170 studies were retrieved from the databases. After excluding studies that did not meet our predefined inclusion criteria, 16 articles were used in this review. More than 400 unique metabolites in blood were shown to be significantly associated with BMD. Of these, three metabolites were reported by ≥ 3 studies, namely valine, leucine and glycine. Glycine was consistently shown to be inversely associated with BMD, while valine was consistently observed to be positively associated with BMD. Inconsistent associations with BMD was observed for leucine.

Summary

With advances in metabolomics technology, an increasing number of metabolites associated with BMD have been identified. Two of these metabolites, namely valine and glycine, were consistently associated with BMD, highlighting their potential for clinical application in osteoporosis. International collaboration with a larger population to conduct clinical studies on these metabolites is warranted. On the other hand, given that metabolomics could be affected by genetics and environmental factors, whether the inconsistent association of the metabolites with BMD is due to the interaction between metabolites and genes and/or lifestyle warrants further study.

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Data Availability

Data supporting this systematic review are available in the Supplementary Information and reference section.

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Salari N, et al. Global prevalence of osteoporosis among the world older adults: a comprehensive systematic review and meta-analysis. J Orthop Surg Res. 2021;16(1):669. https://doi.org/10.1186/s13018-021-02821-8.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Dobbs MB, Buckwalter J, and Saltzman C. Osteoporosis: the increasing role of the orthopaedist. Iowa Orthop J, 1999;19:43–52, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1888612/. Accessed 8 Mar 2023.

  3. Ji M-X, Yu Q. Primary osteoporosis in postmenopausal women. Chronic Dis Transl Med. 2015;1(1):9–13. https://doi.org/10.1016/j.cdtm.2015.02.006.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Global Burden of Disease Collaborative Network, Global Burden of Disease Study 2019 (GBD 2019) Results. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2020. Available from https://vizhub.healthdata.org/gbd-results/. Accessed 13 Mar 2023.

  5. RashkiKemmak A, et al. Economic burden of osteoporosis in the world: A systematic review. Med J Islam Repub Iran. 2020;34:154–154. https://doi.org/10.34171/mjiri.34.154.

    Article  Google Scholar 

  6. Williamson S, et al. Costs of fragility hip fractures globally: a systematic review and meta-regression analysis. Osteoporos Int. 2017;28(10):2791–800. https://doi.org/10.1007/s00198-017-4153-6.

    Article  CAS  PubMed  Google Scholar 

  7. Cheung CL, et al. An updated hip fracture projection in Asia: The Asian Federation of Osteoporosis Societies study. Osteoporos Sarcopenia. 2018;4(1):16–21. https://doi.org/10.1016/j.afos.2018.03.003.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Mei Z, et al. Association between the metabolome and bone mineral density in a Chinese population. EBioMedicine. 2020;62:103111. https://doi.org/10.1016/j.ebiom.2020.103111.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Bernabei R et al. Screening, diagnosis and treatment of osteoporosis: a brief review. Clin Cases Miner Bone Metab, 2014;11(3):201–207, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4269144/. Accessed 13 Mar 2023.

  10. Johnell O, et al. Predictive value of BMD for hip and other fractures. J Bone Miner Res. 2005;20(7):1185–94. https://doi.org/10.1359/JBMR.050304.

    Article  PubMed  Google Scholar 

  11. Kanis JA, et al. Development and use of FRAX in osteoporosis. Osteoporos Int. 2010;21(Suppl 2):S407–13. https://doi.org/10.1007/s00198-010-1253-y.

    Article  PubMed  Google Scholar 

  12. Hippisley-Cox J, Coupland C. Derivation and validation of updated QFracture algorithm to predict risk of osteoporotic fracture in primary care in the United Kingdom: prospective open cohort study. Bmj. 2012;344:e3427. https://doi.org/10.1136/bmj.e3427.

    Article  PubMed  Google Scholar 

  13. Leslie WD, et al. Direct comparison of eight national FRAX® tools for fracture prediction and treatment qualification in Canadian women. Arch Osteoporos. 2013;8(1):145. https://doi.org/10.1007/s11657-013-0145-0.

    Article  CAS  PubMed  Google Scholar 

  14. Miggiels P, et al. Novel technologies for metabolomics: More for less. TrAC Trends Anal Chem. 2019;120:115323. https://doi.org/10.1016/j.trac.2018.11.021.

    Article  CAS  Google Scholar 

  15. Odom JD, Sutton VR. Metabolomics in Clinical Practice: Improving Diagnosis and Informing Management. Clin Chem. 2021;67(12):1606–17. https://doi.org/10.1093/clinchem/hvab184.

    Article  PubMed  Google Scholar 

  16. Miyamoto K et al. A Metabolomic Profile Predictive of New Osteoporosis or Sarcopenia Development. Metabolites, 2021;11(5). https://doi.org/10.3390/metabo11050278.

  17. Miyamoto T, et al. Metabolomics-based profiles predictive of low bone mass in menopausal women. Bone Reports. 2018;9:11–8. https://doi.org/10.1016/j.bonr.2018.06.004.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Aleidi SM, et al. A Distinctive Human Metabolomics Alteration Associated with Osteopenic and Osteoporotic Patients. Metabolites. 2021;11(9):628. https://doi.org/10.3390/metabo11090628.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhang X, et al. Metabolomics Insights into Osteoporosis Through Association With Bone Mineral Density. J Bone Miner Res. 2021;36(4):729–38. https://doi.org/10.1002/jbmr.4240.

    Article  CAS  PubMed  Google Scholar 

  20. Liberati A, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339:b2700. https://doi.org/10.1136/bmj.b2700.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Gong R, et al. Identification and Functional Characterization of Metabolites for Bone Mass in Peri- and Postmenopausal Chinese Women. J Clin Endocrinol Metab. 2021;106(8):e3159–77. https://doi.org/10.1210/clinem/dgab146.

    Article  PubMed  PubMed Central  Google Scholar 

  22. •• Li P, et al. Metabolic Alterations in Older Women With Low Bone Mineral Density Supplemented With Lactobacillus reuteri. JBMR Plus. 2021;5(4):e10478. https://doi.org/10.1002/jbm4.10478. (Study demonstrated the theraputic potential of probiotics supplement on osteoporosis in older women by altering metabolome.)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. • Palacios-González B et al. A Multi-Omic Analysis for Low Bone Mineral Density in Postmenopausal Women Suggests a RELATIONSHIP between Diet, Metabolites, and Microbiota. Microorganisms, 2020;8(11). https://doi.org/10.3390/microorganisms8111630. (Multiomic study revealed the correlation between metabolome, diet and microbiota in Mexican population, leucine and valine were shown to be associated to BMD.)

  24. Pontes TA, et al. Osteopenia-osteoporosis discrimination in postmenopausal women by 1H NMR-based metabonomics. PLoS One. 2019;14(5):e0217348. https://doi.org/10.1371/journal.pone.0217348.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Bellissimo MP, et al. Plasma high-resolution metabolomics identifies linoleic acid and linked metabolic pathways associated with bone mineral density. Clin Nutr. 2021;40(2):467–75. https://doi.org/10.1016/j.clnu.2020.05.041.

    Article  CAS  PubMed  Google Scholar 

  26. Chau Y-P, et al. Serum Metabolome of Coffee Consumption and its Association With Bone Mineral Density: The Hong Kong Osteoporosis Study. J Clin Endocrinol Metab. 2019;105(3):e619–27. https://doi.org/10.1210/clinem/dgz210.

    Article  Google Scholar 

  27. Deng D et al. An Integrated Metabolomic Study of Osteoporosis: Discovery and Quantification of Hyocholic Acids as Candidate Markers. Front Pharmacol, 2021;12. https://doi.org/10.3389/fphar.2021.725341.

  28. Hartley A, et al. Metabolomics analysis in adults with high bone mass identifies a relationship between bone resorption and circulating citrate which replicates in the general population. Clin Endocrinol (Oxf). 2020;92(1):29–37. https://doi.org/10.1111/cen.14119.

    Article  CAS  PubMed  Google Scholar 

  29. He J, et al. Gut microbiota and metabolite alterations associated with reduced bone mineral density or bone metabolic indexes in postmenopausal osteoporosis. Aging. 2020;12(9):8583–604. https://doi.org/10.18632/aging.103168.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. • Ling CW, et al. The Association of Gut Microbiota With Osteoporosis Is Mediated by Amino Acid Metabolism: Multiomics in a Large Cohort. J Clin Endocrinol Metab. 2021;106(10):e3852–64. https://doi.org/10.1210/clinem/dgab492. (Multiomic study of osteoporosis focused on amino acids metabolism and microbiota in a large Chinese cohort.)

    Article  PubMed  Google Scholar 

  31. Mangano KM, et al. Diet-derived fruit and vegetable metabolites show sex-specific inverse relationships to osteoporosis status. Bone. 2021;144:115780. https://doi.org/10.1016/j.bone.2020.115780.

    Article  CAS  PubMed  Google Scholar 

  32. Palacios-Gonzalez B et al. Serum Metabolite Profile Associated with Sex-Dependent Visceral Adiposity Index and Low Bone Mineral Density in a Mexican Population. Metabolites, 2021;11(9). https://doi.org/10.3390/metabo11090604.

  33. Wang J, et al. Discovery of potential biomarkers for osteoporosis using LC-MS/MS metabolomic methods. Osteoporos Int. 2019;30(7):1491–9. https://doi.org/10.1007/s00198-019-04892-0.

    Article  CAS  PubMed  Google Scholar 

  34. Su Y, et al. Circulating amino acids are associated with bone mineral density decline and ten-year major osteoporotic fracture risk in older community-dwelling adults. Bone. 2019;129:115082–115082. https://doi.org/10.1016/j.bone.2019.115082.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Cui Z, et al. Relationship Between Serum Amino Acid Levels and Bone Mineral Density: A Mendelian Randomization Study. Front Endocrinol. 2021;12:763538–763538. https://doi.org/10.3389/fendo.2021.763538.

    Article  Google Scholar 

  36. •• Sartori T, et al. Branched chain amino acids improve mesenchymal stem cell proliferation, reducing nuclear factor kappa B expression and modulating some inflammatory properties. Nutrition. 2020;78:110935. https://doi.org/10.1016/j.nut.2020.110935. (Study demonstrated the effect of BCAAs (including valine and leucine) on mesenchymal stem cell at cellular level.)

    Article  CAS  PubMed  Google Scholar 

  37. Kasagi S, Chen W. TGF-beta1 on osteoimmunology and the bone component cells. Cell Biosci. 2013;3(1):4. https://doi.org/10.1186/2045-3701-3-4.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Amarasekara DS et al. Regulation of Osteoclast Differentiation by Cytokine Networks. Immune Netw, 2018;18(1). https://doi.org/10.4110/in.2018.18.e8.

  39. Lee JH, et al. Anti-inflammatory and anti-genotoxic activity of branched chain amino acids (BCAA) in lipopolysaccharide (LPS) stimulated RAW 264.7 macrophages. Food Sci Biotechnol. 2017;26(5):1371–7. https://doi.org/10.1007/s10068-017-0165-4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Eriksson AL, et al. Serum Glycine Levels Are Associated With Cortical Bone Properties and Fracture Risk in Men. J Clin Endocrinol Metab. 2021;106(12):e5021–9. https://doi.org/10.1210/clinem/dgab544.

    Article  PubMed  Google Scholar 

  41. Jennings A, et al. Amino Acid Intakes Are Associated With Bone Mineral Density and Prevalence of Low Bone Mass in Women: Evidence From Discordant Monozygotic Twins. J Bone Miner Res. 2016;31(2):326–35. https://doi.org/10.1002/jbmr.2703.

    Article  CAS  PubMed  Google Scholar 

  42. Kim MH, Kim HM, Jeong HJ. Estrogen-like osteoprotective effects of glycine in in vitro and in vivo models of menopause. Amino Acids. 2016;48(3):791–800. https://doi.org/10.1007/s00726-015-2127-6.

    Article  CAS  PubMed  Google Scholar 

  43. Chen SY, et al. An NMR metabolomic study on the effect of alendronate in ovariectomized mice. PLoS One. 2014;9(9):e106559. https://doi.org/10.1371/journal.pone.0106559.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Erben V et al. Comparing Metabolomics Profiles in Various Types of Liquid Biopsies among Screening Participants with and without Advanced Colorectal Neoplasms. Diagnostics (Basel), 2021;11(3). https://doi.org/10.3390/diagnostics11030561.

  45. Yu Z, et al. Differences between Human Plasma and Serum Metabolite Profiles. PLOS ONE. 2011;6(7):e21230. https://doi.org/10.1371/journal.pone.0021230.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Suarez-Diez M, et al. Plasma and Serum Metabolite Association Networks: Comparability within and between Studies Using NMR and MS Profiling. J Proteome Res. 2017;16(7):2547–59. https://doi.org/10.1021/acs.jproteome.7b00106.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Pharmacology and Pharmacy, The University of Hong Kong, 21 Sassoon Road, Pokfulam, Hong Kong

    Kat-Tik Lau, Suhas Krishnamoorthy, Chor-Wing Sing & Ching Lung Cheung

  2. Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Pak Shek Kok, Hong Kong

    Ching Lung Cheung

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Dr. Cheung received honorarium and research support from Amgen. Other authors declare no competing interests.

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Lau, KT., Krishnamoorthy, S., Sing, CW. et al. Metabolomics of Osteoporosis in Humans: A Systematic Review. Curr Osteoporos Rep 21, 278–288 (2023). https://doi.org/10.1007/s11914-023-00785-8

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