Investigation of the relationships between knee osteoarthritis and obesity via untargeted metabolomics analysis

Abstract

Objective

Osteoarthritis (OA), the most encountered arthritis form, result from degeneration of articular cartilage. Obesity is accepted as a significant risk factor for knee OA (KOA). In this study, it is aimed to determine the variation of metabolites between control and patients with KOA and observe the effect of obesity on KOA via untargeted metabolomics method.

Methods

Serum samples of following groups were collected: patient group including 14 obesity (OKOA) and 14 non-obesity (NOKOA) (n = 28) and control group (n = 15) from orthopedics and traumatology policlinic. Serum proteins were denatured by acetonitrile and chromatographic separation of metabolites was achieved by LC/Q-TOF/MS/MS method. Data acquisition, classification, and identification were achieved by METLIN database. Cluster analysis was performed with MATLAB2017a-PLS Toolbox 7.2.

Results

Obtained results showed that 244 (patient vs control) and 274 (OKOA vs NOKOA) m/z ratios were determined in accordance with LC/Q-TOF/MS/MS analysis. Multivariate data analysis was applied 41 and 36 m/z signal (p ≤ 0.01; fold analysis > 1.5) were filtered for patient vs control group and OKOA vs NOKOA, respectively. Twenty-one different metabolites were identified for patient vs control group and 15 metabolites were determined for OKOA vs NOKOA group.

Conclusion

Acid concentration and oxidative stress agents were high in inflammation group and their levels were much higher in obesity. It is claimed that obesity cause oxidative stress and acidosis in arthritis patients. Valine was found to be the only BCAA molecule whose concentration has significantly different in KOA patients. The relation between KOA and obesity was firstly investigated with metabolomics method.

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References

  1. 1.

    Grainger R, Cicuttini FM (2004) Medical management of osteoarthritis of the knee and hip joints. Med J Aust 180(5):232–236

    PubMed  Article  Google Scholar 

  2. 2.

    Johnson VL, Hunter DJ (2014) The epidemiology of osteoarthritis. Best Pract Res Clin Rheumatol 28(1):5–15

    PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Richter M, Trzeciak T, Owecki M, Pucher A, Kaczmarczyk J (2015) The role of adipocytokines in the pathogenesis of knee joint osteoarthritis. Int Orthop 39(6):1211–1217

    PubMed  Article  Google Scholar 

  4. 4.

    Won Y, Shin Y, Chun CH, Cho Y, Ha CW, Kim JH, Chun JS (2016) Pleiotropic roles of metallothioneins as regulators of chondrocyte apoptosis and catabolic and anabolic pathways during osteoarthritis pathogenesis. Ann Rheum Dis 75(11):2045–2052

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Chauffier K, Laiguillon MC, Bougault C, Gosset M, Priam S, Salvat C, Mladenovic Z, Nourissat G, Jacques C, Houard X, Berenbaum F, Sellam J (2012) Induction of the chemokine IL-8/Kc by the articular cartilage: possible influence on osteoarthritis. Joint Bone Spine 79(6):604–609

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Oh H, Kwak JS, Yang S, Gong MK, Kim JH, Rhee J, Kim SK, Kim HE, Ryu JH, Chun JS (2015) Reciprocal regulation by hypoxia-inducible factor-2alpha and the NAMPT-NAD(+)-SIRT axis in articular chondrocytes is involved in osteoarthritis. Osteoarthr Cartil 23(12):2288–2296

    CAS  PubMed  Article  Google Scholar 

  7. 7.

    Takaishi H, Kimura T, Dalal S, Okada Y, D'Armiento J (2008) Joint diseases and matrix metalloproteinases: a role for MMP-13. Curr Pharm Biotechnol 9(1):47–54

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Conde J, Scotece M, Gomez R, Lopez V, Gomez-Reino JJ, Gualillo O (2011) Adipokines and osteoarthritis: novel molecules involved in the pathogenesis and progression of disease. Arthritis 2011:203901, 1, 8

  9. 9.

    Hu PF, Bao JP, Wu LD (2011) The emerging role of adipokines in osteoarthritis: a narrative review. Mol Biol Rep 38(2):873–878

    CAS  PubMed  Article  Google Scholar 

  10. 10.

    Al Haj Ahmad RM, Al-Domi HA (2017) Complement 3 serum levels as a pro-inflammatory biomarker for insulin resistance in obesity. Diabetes Metab Syndr 11(Suppl 1):S229–S232

    PubMed  Article  Google Scholar 

  11. 11.

    McNulty AL, Miller MR, O'Connor SK, Guilak F (2011) The effects of adipokines on cartilage and meniscus catabolism. Connect Tissue Res 52(6):523–533

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Yusuf E, Nelissen RG, Ioan-Facsinay A, Stojanovic-Susulic V, DeGroot J, van Osch G, Middeldorp S, Huizinga TW, Kloppenburg M (2010) Association between weight or body mass index and hand osteoarthritis: a systematic review. Ann Rheum Dis 69(4):761–765

    PubMed  Article  Google Scholar 

  13. 13.

    Pottie P, Presle N, Terlain B, Netter P, Mainard D, Berenbaum F (2006) Obesity and osteoarthritis: more complex than predicted! Ann Rheum Dis 65(11):1403–1405

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14.

    Bijlsma JW, Berenbaum F, Lafeber FP (2011) Osteoarthritis: an update with relevance for clinical practice. Lancet 377(9783):2115–2126

    PubMed  PubMed Central  Article  Google Scholar 

  15. 15.

    Longnecker K, Futrelle J, Coburn E, Soule MCK, Kujawinski EB (2015) Environmental metabolomics: databases and tools for data analysis. Mar Chem 177:366–373

    CAS  Article  Google Scholar 

  16. 16.

    Trivedi DK, Hollywood KA, Goodacre R (2017) Metabolomics for the masses: the future of metabolomics in a personalized world. New Horiz Transl Med 3(6):294–305

    PubMed  PubMed Central  Google Scholar 

  17. 17.

    Teo CC, Chong WPK, Tan E, Basri NB, Low ZJ, Ho YS (2015) Advances in sample preparation and analytical techniques for lipidomics study of clinical samples. TrAC Trends Anal Chem 66:1–18

    CAS  Article  Google Scholar 

  18. 18.

    Giera M, Ioan-Facsinay A, Toes R, Gao F, Dalli J, Deelder AM, Serhan CN, Mayboroda OA (2012) Lipid and lipid mediator profiling of human synovial fluid in rheumatoid arthritis patients by means of LC–MS/MS. Biochim Biophys Acta 1821(11):1415–1424

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Wishart DS (2016) Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov 15(7):473–484

    CAS  PubMed  Article  Google Scholar 

  20. 20.

    Suzuki M, Nishiumi S, Matsubara A, Azuma T, Yoshida M (2014) Metabolome analysis for discovering biomarkers of gastroenterological cancer. J Chromatogr B Anal Technol Biomed Life Sci 966:59–69

    CAS  Article  Google Scholar 

  21. 21.

    Zhang P, Georgiou CA, Brusic V (2018) Elemental metabolomics. Brief Bioinform 19(3):524–536

    PubMed  Article  CAS  Google Scholar 

  22. 22.

    Adams SB Jr, Setton LA, Nettles DL (2013) The role of metabolomics in osteoarthritis research. J Am Acad Orthop Surg 21(1):63–64

    PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Norman B, Davison A, Wilson P, Ross G, Milan A, Roberts N, Ranganath L, Gallagher J (2017) Urine metabolomics using liquid chromatography quadrupole time-of-flight mass spectrometry indicates common markers of disease in alkaptonuria and idiopathic osteoarthritis in human. Osteoarthr Cart 25:S97–S98

    Article  Google Scholar 

  24. 24.

    Zhang W, Likhodii S, Zhang Y, Aref-Eshghi E, Harper PE, Randell E, Green R, Martin G, Furey A, Sun G (2014) Classification of osteoarthritis phenotypes by metabolomics analysis. BMJ Open 4(11):e006286

    PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Altman R, Asch E, Bloch D, Bole G, Borenstein D, Brandt K, Christy W, Cooke TD, Greenwald R, Hochberg M et al (1986) Development of criteria for the classification and reporting of osteoarthritis. Classification of osteoarthritis of the knee. Diagnostic and Therapeutic Criteria Committee of the American Rheumatism Association. Arthritis Rheum 29(8):1039–1049

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Worley B, Powers R (2013) Multivariate analysis in metabolomics. Current Metabolomics 1(1):92–107

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Wei X, Shi X, Kim S, Zhang L, Patrick JS, Binkley J, McClain C, Zhang X (2012) A data pre-processing method for liquid chromatography mass spectrometry-based metabolomics. Anal Chem 84(18):7963–7971

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28.

    Sellam J, Berenbaum F (2012) Osteoarthritis and obesity. La Revue du praticien 62(5):621–624

    PubMed  Google Scholar 

  29. 29.

    Bray GA (2004) Medical consequences of obesity. J Clin Endocrinol Metab 89(6):2583–2589

    CAS  PubMed  Article  Google Scholar 

  30. 30.

    Sinusas K (2012) Osteoarthritis: diagnosis and treatment. Am Fam Physician 85(1):49–56

  31. 31.

    Shi L (2017) Untargeted metabolomics and novel data analysis strategies to identify biomarkers of diet and type 2 diabetes

  32. 32.

    Psychogios N, Hau DD, Peng J, Guo AC, Mandal R, Bouatra S, Sinelnikov I, Krishnamurthy R, Eisner R, Gautam B, Young N, Xia J, Knox C, Dong E, Huang P, Hollander Z, Pedersen TL, Smith SR, Bamforth F, Greiner R, McManus B, Newman JW, Goodfriend T, Wishart DS (2011) The human serum metabolome. PLoS One 6(2):e16957

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Kosinska MK, Liebisch G, Lochnit G, Wilhelm J, Klein H, Kaesser U, Lasczkowski G, Rickert M, Schmitz G, Steinmeyer J (2014) Sphingolipids in human synovial fluid - a lipidomic study. PLoS One 9(3):e91769

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  34. 34.

    Drissi F, Merhej V, Angelakis E, El Kaoutari A, Carrière F, Henrissat B, Raoult D (2014) Comparative genomics analysis of lactobacillus species associated with weight gain or weight protection. Nutr Diabetes 4(2):e109

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. 35.

    Gogna N, Krishna M, Oommen AM, Dorai K (2015) Investigating correlations in the altered metabolic profiles of obese and diabetic subjects in a South Indian Asian population using an NMR-based metabolomic approach. Mol BioSyst 11(2):595–606

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Rizza RA, Mandarino LJ, Gerich JE (1982) Cortisol-induced insulin resistance in man: impaired suppression of glucose production and stimulation of glucose utilization due to a postreceptor detect of insulin action. J Clin Endocrinol Metab 54(1):131–138

    CAS  PubMed  Article  Google Scholar 

  37. 37.

    de Paz-Lugo P, Lupianez JA, Melendez-Hevia E (2018) High glycine concentration increases collagen synthesis by articular chondrocytes in vitro: acute glycine deficiency could be an important cause of osteoarthritis. Amino Acids 50(10):1357–1365

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  38. 38.

    IGARI T, TSUCHIZAWA M, SHIMAMURA T (1987) Alteration of tryptophan metabolism in the synovial fluid of patients with rheumatoid arthritis and osteoarthritis. Tohoku J Exp Med 153(2):79–86

    CAS  PubMed  Article  Google Scholar 

  39. 39.

    Li Y, Xiao W, Luo W, Zeng C, Deng Z, Ren W, Wu G, Lei G (2016) Alterations of amino acid metabolism in osteoarthritis: its implications for nutrition and health. Amino Acids 48(4):907–914

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Blanco FJ, Valdes AM, Rego-Perez I (2018) Mitochondrial DNA variation and the pathogenesis of osteoarthritis phenotypes. Nat Rev Rheumatol 14(6):327–340

    CAS  PubMed  Article  Google Scholar 

  41. 41.

    Yang G, Zhang H, Chen T, Zhu W, Ding S, Xu K, Xu Z, Guo Y, Zhang J (2016) Metabolic analysis of osteoarthritis subchondral bone based on UPLC/Q-TOF-MS. Anal Bioanal Chem 408(16):4275–4286

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Levick JR (1990) Hypoxia and acidosis in chronic inflammatory arthritis; relation to vascular supply and dynamic effusion pressure. J Rheumatol 17(5):579–582

    CAS  PubMed  Google Scholar 

  43. 43.

    Collins JA, Moots RJ, Winstanley R, Clegg PD, Milner PI (2013) Oxygen and pH-sensitivity of human osteoarthritic chondrocytes in 3-D alginate bead culture system. Osteoarthr Cartil 21(11):1790–1798

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Jennings A, MacGregor A, Pallister T, Spector T, Cassidy A (2016) Associations between branched chain amino acid intake and biomarkers of adiposity and cardiometabolic health independent of genetic factors: a twin study. Int J Cardiol 223:992–998

    PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Shah SH, Bain JR, Muehlbauer MJ, Stevens RD, Crosslin DR, Haynes C, Dungan J, Newby LK, Hauser ER, Ginsburg GS, Newgard CB, Kraus WE (2010) Association of a peripheral blood metabolic profile with coronary artery disease and risk of subsequent cardiovascular events. Circ Cardiovasc Genet 3(2):207–214

    CAS  PubMed  Article  Google Scholar 

  46. 46.

    Rockel J, Kapoor M (2018) The metabolome and osteoarthritis: possible contributions to symptoms and pathology. Metabolites 8(4):92

    PubMed Central  Article  CAS  Google Scholar 

  47. 47.

    Anderson JR, Chokesuwattanaskul S, Phelan MM, Welting TJ, Lian L-Y, Peffers MJ, Wright HL (2018) 1H NMR metabolomics identifies underlying inflammatory pathology in osteoarthritis and rheumatoid arthritis synovial joints. J Proteome Res 17(11):3780–3790

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgments

We thank all the study participants who made this study possible, and all the staff who helped us in the collection of samples and East Anatolia High Technology Application and Research Center (DAYTAM) for their kind contribution in Q-TOF analysis. We all thank to the contribution of Hospital La Fe Metabolomics Group, Prof. Dr. Maximo Vento, Dr. Julia Kuligowski and Dr. Guillermo Quintas.

Author contributors

Study design, OS, GG, FDM; collection of blood samples, KG; experiments, FDM, OS; metabolite profiling assay, GG; statistical analysis, GG, OS; writing of the manuscript, GG, FDM, OS, KG. All authors contributed to the critical comment on the final manuscript and approved the final manuscript.

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Correspondence to Gulsah Gundogdu.

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This study was conducted with the approval of the Ataturk University Ethics Committee.

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Senol, O., Gundogdu, G., Gundogdu, K. et al. Investigation of the relationships between knee osteoarthritis and obesity via untargeted metabolomics analysis. Clin Rheumatol 38, 1351–1360 (2019). https://doi.org/10.1007/s10067-019-04428-1

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Keywords

  • Knee osteoarthritis
  • LC/Q-TOF/MS/MS
  • Untargeted metabolomics