Skip to main content

The GC–MS metabolomics signature in patients with fibromyalgia syndrome directs to dysbiosis as an aspect contributing factor of FMS pathophysiology



Fibromyalgia syndrome (FMS) is a chronic pain syndrome. Previous analyses of untargeted metabolomics data indicated altered metabolic profile in FMS patients.


We report a semi-targeted explorative metabolomics study on the urinary metabolite profile of FMS patients; exploring the potential of urinary metabolite information to augment existing medical diagnosis.


All cases were females. Patients had a medical history of persistent FMS (n = 18). Control groups were first-generation family members of the patients (n = 11), age-related individuals without indications of FMS (n = 10), and healthy, young (18–22 years) individuals (n = 41). The biofluid investigated was early morning urine samples. Data generation was done through gas chromatography–mass spectrometry (GC–MS) analysis and data processing and analyses were performed using Matlab, R, SPSS and SAS software.


Quantitative analysis revealed the presence of 196 metabolites. Unsupervised and supervised multivariate analyses distinguished all three control groups and the FMS patients, which could be related to 14 significantly increased metabolites. These metabolites are associated with energy metabolism, digestion and metabolism of carbohydrates and other host and gut metabolites.


Overall, urinary metabolite profiles in the FMS patients suggest: (1) energy utilization is a central aspect of this pain disorder, (2) dysbiosis seems to prevail in FMS patients, indicated by disrupted microbiota metabolites, supporting the model that microbiota may alter brain function through the gut-brain axis, with the gut being a gateway to generalized pain, and (3) screening of urine from FMS is an avenue to explore for adding non-invasive clinical information for diagnosis and treatment of FMS.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Data availability

The clinical details on the patients, as well as the full complement of NMR metabolomics data, are available in the Supplementary Information of (Malatji et al. 2017).



2-Deoxy-3,5-dihydroxypentanoic lactone




4-Hydroxybutyric acid


American College of Rheumatology






Bonferroni–Holm test




Group of first-degree relatives of the patients


Group of healthy young subjects


Group of age-matched subjects but unrelated to the patients


Central nervous system

EI and HP:

A general and a histidine-containing cytoplasmic phosphor-carrier bacterial protein system


Tagatose-specific B. licheniformis multi-domain membrane proteins


Enzyme Commission number


Effect size


Fold change


Fibromyalgia impact questionnaire




Fibromyalgia syndrome


Functional pain syndromes


Galactonic acid-lactone


Gas chromatographic–mass spectrometric


2-Hydroxy-glutaric acid


Human metabolome database


Irritable bowel syndrome


In-house clinical questionnaire


Malic acid


Mann–Whitney test


National Institute of Standards and Technology


Proton nuclear magnetic resonance


Nuclear Technologies in Medicine and Biosciences Initiative


North-West University


High-energy phosphate


Principal component


Principal components analysis


Partial least squares discriminant analysis


Phosphoenolpyruvate:carbohydrate phosphotransferase system


Predictive ability parameter


Goodness-of-fit parameter


Small intestinal bacterial over-growth






Tagatose-1-phosphate kinase






Variable important in projection


  • Baumgart, D. C., & Carding, S. R. (2007). Inflammatory bowel disease: Cause and immunobiology. Lancet, 369(9573), 1627–1640.

    CAS  PubMed  Article  Google Scholar 

  • Bennett, R. M., Friend, R., Jones, K. D., Ward, R., Han, B. K., & Ross, R. L. (2009). The revised fibromyalgia impact questionnaire (FIQR): Validation and psychometric properties. Arthritis Research & Therapy, 11(4), R120.

    Article  Google Scholar 

  • Bouatra, S., Aziat, F., Mandal, R., Guo, A. C., Wilson, M. R., Knox, C., et al. (2013). The human urine metabolome. PLOS ONE, 8(9), e73076.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Brown, G. K., Cromby, C. H., Manning, N. J., & Pollitt, R. J. (1987). Urinary organic acids in succinic semialdehyde dehydrogenase deficiency: Evidence of α-oxidation of 4-hydroxybutyric acid, interaction of succinic semialdehyde with pyruvate dehydrogenase and possible secondary inhibition of mitochondrial β-oxidation. Journal of Inherited Metabolic Disease, 10(4), 367–375.

    CAS  PubMed  Article  Google Scholar 

  • Caboni, P., Liori, B., Kumar, A., Santoru, M. L., Asthana, S., Pieroni, E., et al. (2014). Metabolomics analysis and modeling suggest a Lysophosphocholines-PAF receptor interaction in fibromyalgia. PLoS ONE, 9(9), e107626.

    PubMed  PubMed Central  Article  Google Scholar 

  • Carabotti, M., Scirocco, A., Maselli, M. A., & Severi, C. (2015). The gut-brain axis: Interactions between enteric microbiota, central and enteric nervous systems. Annals of Gastroenterology, 28(2), 203–209.

    PubMed  PubMed Central  Google Scholar 

  • Clauw, D. J. (2014). Fibromyalgia: A clinical review. JAMA, 311(15), 1547–1555.

    PubMed  Article  Google Scholar 

  • Clauw, D. J. (2015). Fibromyalgia and related conditions. Mayo Clinic Proceedings, 90(5), 680–692.

    PubMed  Article  Google Scholar 

  • Clish, C. B. (2015). Metabolomics: An emerging but powerful tool for precision medicine. Molecular Case Studies, 1(1), a000588.

    PubMed  Article  Google Scholar 

  • Cryan, J. F., & Dinan, T. G. (2012). Mind-altering microorganisms: The impact of the gut microbiota on brain and behaviour. Nature Reviews Neuroscience, 13(10), 701–712.

    CAS  PubMed  Article  Google Scholar 

  • Cryan, J. F., & O’Mahony, S. M. (2011). The microbiome-gut-brain axis: From bowel to behavior. Journal of Neurogastroenterology and Motility, 23(3), 187–192.

    CAS  Article  Google Scholar 

  • Egloff, N., Von Känel, R., Müller, V., Egle, U. T., Kokinogenis, G., Lederbogen, S., et al. (2015). Implications of proposed fibromyalgia criteria across other functional pain syndromes. Scandinavian Journal of Rheumatology, 44(5), 416–424.

    CAS  PubMed  Article  Google Scholar 

  • Foster, J. A., & Neufeld, K. A. (2013). Gut–brain axis: How the microbiome influences anxiety and depression. Trends in Neurosciences, 36(5), 305–312.

    CAS  PubMed  Article  Google Scholar 

  • Ge, Y., Duan, Y., Fang, G., Zhang, Y., & Wang, S. (2009). Polysaccharides from fruit calyx of Physalis alkekengi var. francheti: Isolation, purification, structural features and antioxidant activities. Carbohydrate Polymers, 77(2), 188–193.

    CAS  Article  Google Scholar 

  • Hackshaw, K. V., Rodriguez-Saona, L., Plans, M., Bell, L. N., & Buffington, C. T. (2013). A bloodspot-based diagnostic test for fibromyalgia syndrome and related disorders. Analyst, 138(16), 4453–4462.

    CAS  PubMed  Article  Google Scholar 

  • Hansen, J., Gulati, A., & Sartor, R. B. (2010). The role of mucosal immunity and host genetics in defining intestinal commensal bacteria. Current Opinion in Gastroenterology, 26(6), 564–571.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Henström, M., & D’Amato, M. (2016). Genetics of irritable bowel syndrome. Molecular and Cellular Neuroscience, 3(1), 7.

    Google Scholar 

  • Högenauer, C., Hammer, H. F., Krejs, G. J., & Reisinger, C. (1998). Mechanisms and management of antibiotic-associated diarrhea. Clinical Infectious Diseases, 27(4), 702–710.

    PubMed  Article  Google Scholar 

  • Isono, M., Nakanishi, I., Sasajima, K. I., Motizuki, K., Kanzaki, T., Okazaki, H., et al. (1968). 2-Keto-l-gulonic acid fermentation: Part I. Paper chromatographic characterization of metabolic products from sorbitol and l-sorbose by various bacteria. Agricultural Biological Chemistry, 32(4), 424–431.

    CAS  Google Scholar 

  • Jackson, F., Georgakopoulou, N., Kaluarachchi, M., Kyriakides, M., Andreas, N., Przysiezna, N., et al. (2016). Development of a pipeline for exploratory metabolic profiling of infant urine. Journal of Proteome Research, 15(9), 3432–3440.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Jones, K. D., Gelbart, T., Whisenant, T. C., Waalen, J., Mondala, T. S., Iklé, D. N., et al. (2016). Genome-wide expression profiling in the peripheral blood of patients with fibromyalgia. Clinical and Experimental Rheumatology, 34(2 Suppl 96), 89–98.

    PubMed Central  Google Scholar 

  • Kelker, N. E., Simkins, R. A., & Anderson, R. L. (1972). Pathway of l-sorbose metabolism in Aerobacter aerogenes. Journal of Biological Chemistry, 247(5), 1479–1483.

    CAS  PubMed  Google Scholar 

  • Kennedy, P. J., Cryan, J. F., Dinan, T. G., & Clarke, G. (2014). Irritable bowel syndrome: A microbiome-gut-brain axis disorder? World Journal of Gastroenterology, 20(39), 14105–14125.

    PubMed  PubMed Central  Article  Google Scholar 

  • Kundig, W., Ghosh, S., & Roseman, S. (1964). Phosphate bound to histidine in a protein as an intermediate in a novel phospho-transferase system. PNAS, 52(4), 1067–1074.

    CAS  PubMed  Article  Google Scholar 

  • Legangneux, E., Mora, J. J., Spreux-Varoquaux, O., Thorin, I., Herrou, M., Alvado, G., et al. (2001). Cerebrospinal fluid biogenic amine metabolites, plasma-rich platelet serotonin and [3H]imipramine reuptake in the primary fibromyalgia syndrome. Rheumatology, 40, 290–296.

    CAS  PubMed  Article  Google Scholar 

  • Levine, J. S., & Burakoff, R. (2011). Extraintestinal manifestations of inflammatory bowel disease. Gastroenterology & Hepatology (NY), 7(4), 235–241.

    Google Scholar 

  • Li, M., Wang, B., Zhang, M., Rantalainen, M., Wang, S., Zhou, H., et al. (2008). Symbiotic gut microbes modulate human metabolic phenotypes. PNAS, 105(6), 2117–2122.

    CAS  PubMed  Article  Google Scholar 

  • Malatji, B. G., Meyer, H., Mason, S., Engelke, U. F., Wevers, R. A., van Reenen, M., et al. (2017). A diagnostic biomarker profile for fibromyalgia syndrome based on an NMR metabolomics study of selected patients and controls. BMC Neurology, 17(1), 88.

    PubMed  PubMed Central  Article  Google Scholar 

  • Mason, S., Moutloatse, G. P., van Furth, M. A., Solomons, R., van Reenen, M., Reinecke, C. J., et al. (2014). KEMREP: A new qualitative method for the assessment of an analyst’s ability to generate a metabolomics data matrix by gas chromatography–mass spectrometry. Current Metabolomics, 2(1), 15–26.

    CAS  Article  Google Scholar 

  • Mayer, E. A., & Tillisch, K. (2011). The brain-gut axis in abdominal pain syndromes. Annual Review of Medicine, 62, 381–396.

    CAS  PubMed  Article  Google Scholar 

  • Mayer, E. A., Tillisch, K., & Gupta, A. (2015). Gut/brain axis and the microbiota. Journal of Clinical Investigation, 125(3), 926–938.

    PubMed  Article  Google Scholar 

  • McFarland, L. V. (2014). Use of probiotics to correct dysbiosis of normal microbiota following disease or disruptive events: A systematic review. British Medical Journal Open, 4(8), e005047.

    Google Scholar 

  • Medina, S., Domínguez-Perles, R., Ferreres, F., Tomás-Barberán, F. A., & Gil-Izquierdo, Á. (2013). The effects of the intake of plant foods on the human metabolome. Trends in Analytical Chemistry, 52, 88–99.

    CAS  Article  Google Scholar 

  • Meyer, H. P. (2002). Myofascial pain syndrome and its suggested role in the pathogenesis and treatment of fibromyalgia syndrome. Current Pain and Headache Reports, 6(4), 274–283.

    PubMed  Article  Google Scholar 

  • Miquel, S., Leclerc, M., Martin, R., Chain, F., Lenoir, M., Raguideau, S., et al. (2015). Identification of metabolic signatures linked to anti-inflammatory effects of Faecalibacterium prausnitzii. mBio, 6(2), e00300-15.

    PubMed  PubMed Central  Article  Google Scholar 

  • Moon, J. Y., Jung, H. J., Moon, M. H., Chung, B. C., & Choi, M. H. (2009). Heat-map visualization of gas chromatography-mass spectrometry based quantitative signatures on steroid metabolism. Journal of the American Society for Mass Spectrometry, 20(9), 1626–1637.

    CAS  PubMed  Article  Google Scholar 

  • Murphey, W. H., & Rosenblum, E. D. (1964). Mannitol catabolism by Staphylococcus aureus. Archives of Biochemistry and Biophysics, 107(2), 292–297.

    CAS  PubMed  Article  Google Scholar 

  • Nicholson, J. K., Holmes, E., Kinross, J., Burcelin, R., Gibson, G., Jia, W., et al. (2012). Host-gut microbiota metabolic interactions. Science, 336(6086), 1262–1267.

    CAS  Article  Google Scholar 

  • Nicholson, J. K., Holmes, E., & Wilson, I. D. (2005). Gut microorganisms, mammalian metabolism and personalized health care. Nature Reviews Microbiology, 3(5), 431–438.

    CAS  PubMed  Article  Google Scholar 

  • Pimentel, M., Chow, E. J., Hallegua, D., Wallace, D., & Lin, H. C. (2001). Small intestinal bacterial overgrowth: A possible association with fibromyalgia. Journal of Musculoskeletal Pain, 9(3), 105–113.

    Article  Google Scholar 

  • Postma, P. W., & Lengeler, J. W. (1985). Phosphoenolpyruvate: Carbohydrate phosphotransferase system of bacteria. Microbiological Reviews, 49(3), 232–269.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Russell, I. J., Orr, M. D., Littman, B., Vipraio, G. A., Alboukrek, D., Michalek, J. E., et al. (1994). Elevated cerebrospinal fluid levels of substance P in patients with the fibromyalgia syndrome. Arthritis & Rheumatology, 37(11), 1593–1601.

    CAS  Article  Google Scholar 

  • Saier, M. H., Jr. (2015). The bacterial phosphotransferase system: New frontiers 50 years after its discovery. Journal of Molecular Microbiology and Biotechnology, 25(2–3), 73–78.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Shah, J. P., Phillips, T. M., Danoff, J. V., & Gerber, L. H. (2005). An in vivo microanalytical technique for measuring the local biochemical milieu of human skeletal muscle. Journal of Applied Physiology, 99(5), 1977–1984.

    CAS  PubMed  Article  Google Scholar 

  • van der Heiden, E., Delmarcelle, M., Lebrun, S., Freichels, R., Brans, A., Vastenavond, C. M., et al. (2013). A pathway closely related to the d-tagatose pathway of Gram-negative enterobacteria identified in the Gram-positive bacterium Bacillus licheniformis. Applied and Environmental Microbiology, 79(11), 3511–3515.

    PubMed  PubMed Central  Article  Google Scholar 

  • van Schaik, W., & Willems, R. J. (2010). Genome-based insights into the evolution of enterococci. Clinical Microbiology & Infection, 16(6), 527–532.

    Article  Google Scholar 

  • Wallace, D. J., & Hallegua, D. S. (2004). Fibromyalgia: The gastrointestinal link. Current Pain and Headache Reports, 8(5), 364–368.

    PubMed  Article  Google Scholar 

  • Willats, W. G. T., McCartney, L., Mackie, W., & Knox, J. P. (2001). Pectin: Cell biology and prospects for functional analysis. Plant Molecular Biology, 47(1–2), 9–27.

    CAS  PubMed  Article  Google Scholar 

  • Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M., Knox, C., Liu, Y., et al. (2012). HMDB 3.0—The human metabolome database in 2013. Nucleic Acids Research, 41(D1), D801–D807.

    PubMed  PubMed Central  Article  Google Scholar 

  • Wolfe, F., Smythe, H. A., Yunus, M. B., Bennett, R. M., Bombardier, C., Goldenberg, D. L., et al. (1990). The American College of Rheumatology 1990 criteria for the classification of fibromyalgia. Arthritis & Rheumatology, 33(2), 160–172.

    CAS  Article  Google Scholar 

  • Yunus, M. B., Dailey, J. W., Aldag, J. C., Masi, A. T., & Jobe, P. C. (1992). Plasma and urinary catecholamines in primary fibromyalgia: A controlled study. Journal of Rheumatology, 19, 95–97.

    CAS  PubMed  Google Scholar 

Download references


Research funding for this project was provided by the Technological Innovation Agency (TIA) of the South African Department of Science and Technology (DST) and from the Nuclear Technologies in Medicine and the Biosciences Initiative (NTeMBI) of the Nuclear Energy Corporation of South Africa (NECSA). BM received a postgraduate bursary from the National Research Foundation (NRF) of South Africa.

Author information

Authors and Affiliations



This investigation required a multidisciplinary approach and the inputs of all authors were essential to produce the concept and final manuscript. CR and HM defined the aim of the study. The urine samples from the FMS patients and age-matched and family-related controls were provided by HM, who also performed all relevant clinical aspects, and acted as assistant promoter to BM. CR developed the experimental design, acted as the promoter for BM and arranged with HM for ethical approval for the study and for the collection of samples from the young controls. BM conducted all experimental analyses and compiled the clinical information provided by HM. LJM gave guidance and assessment on GC–MS data generation and analyses. RW was responsible for critical evaluation of the analytical aspects of the clinical chemistry data and for their interpretation. MvR performed all the statistical analyses. SM was responsible for coordination and integration of inputs from the authors who contributed to the manuscript as presented here.

Corresponding author

Correspondence to Shayne Mason.

Ethics declarations

Conflict of interest

The authors declare that there are no competing interests regarding the publication of this paper.

Ethical approval

Ethical approval for the study was obtained via a consortium under the Nuclear Technologies in Medicine and Biosciences Initiative (NTeMBI) of South Africa and ethical approval by Pharma Ethics Pty, Ltd (Reference number 11064365). Pharma Ethics confirmed the following: “The study has been accepted as complying to the Ethics Standards for Clinical Research with a new drug in participants based on FDA, ICH GCP and the Declaration of Helsinki guidelines. The Ethics Committee (IRB) granting this APPROVAL is in compliance with the Guidelines for Good Practice in the Conduct of Clinical Trials in Human Participants in South Africa (2006), ICH Harmonised Tripartite Guidelines E6: Note: for the Guidance in Good Clinical Practice (CPMP/ICH/135/95) and FDA Code of Federal Regulation Part 50, 56 and 312.” Informed consent was obtained from all the participants in this study by means of a voluntarily completed consent form, included in the SI.

Informed consent

All authors have given their approval of the version of the manuscript as submitted, their consent for publication and agreed to the accountability requirements.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 1377 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Malatji, B.G., Mason, S., Mienie, L.J. et al. The GC–MS metabolomics signature in patients with fibromyalgia syndrome directs to dysbiosis as an aspect contributing factor of FMS pathophysiology. Metabolomics 15, 54 (2019).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Fibromyalgia syndrome (FMS)
  • Gas chromatography–mass spectrometry (GC–MS)
  • Dysbiosis
  • Carbohydrate
  • Pain
  • Biomarkers