Complement Component C1q as a Potential Diagnostic Tool for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Subtyping

Jesús Castro-Marrero, Mario Zacares, Eloy Almenar-Pérez, José Alegre-Martín, Elisa Oltra, Jesús Castro-Marrero, Mario Zacares, Eloy Almenar-Pérez, José Alegre-Martín, Elisa Oltra

Abstract

Background: Routine blood analytics are systematically used in the clinic to diagnose disease or confirm individuals' healthy status. For myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), a disease relying exclusively on clinical symptoms for its diagnosis, blood analytics only serve to rule out underlying conditions leading to exerting fatigue. However, studies evaluating complete and large blood datasets by combinatorial approaches to evidence ME/CFS condition or detect/identify case subgroups are still scarce.

Methods: This study used unbiased hierarchical cluster analysis of a large cohort of 250 carefully phenotyped female ME/CFS cases toward exploring this possibility.

Results: The results show three symptom-based clusters, classified as severe, moderate, and mild, presenting significant differences (p < 0.05) in five blood parameters. Unexpectedly the study also revealed high levels of circulating complement factor C1q in 107/250 (43%) of the participants, placing C1q as a key molecule to identify an ME/CFS subtype/subgroup with more apparent pain symptoms.

Conclusions: The results obtained have important implications for the research of ME/CFS etiology and, most likely, for the implementation of future diagnosis methods and treatments of ME/CFS in the clinic.

Keywords: C1q; blood analytics; chronic fatigue syndrome; cluster analysis; complement system; diagnosis; myalgic encephalomyelitis; symptoms.

Conflict of interest statement

The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Graphic representation of ME/CFS clustering according to symptom standard mean scores.
Figure 2
Figure 2
Blood analytic difference boxplots between ME/CFS symptom-based clusters. Abbreviations: Hb, hemoglobin; NT, neutrophil counts; COL, cholesterol; HDL, high-density lipoprotein; C3, complement 3. The significance level was set at * p < 0.05. Data beyond 1.5 inter-quartile range values, representing potential outliers, are plotted as individual dots.
Figure 3
Figure 3
Graphic representation of ME/CFS symptom standard score differences in relation to C1q stratification.

References

    1. WHO . ICD-10: International Classification of Statistical Classification of Diseases and Related Health Problems. World Health Organization; Geneva, Switzerland: 2004.
    1. World Health Organization. Committee on the Diagnostic Criteria for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Board on the Health of Select Populations. Institute of Medicine . Beyond Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: Redefining an Illness. National Academies Press; Washington, DC, USA: 2015.
    1. Estévez-López F., Castro-Marrero J., Wang X., Bakken I.J., Ivanovs A., Nacul L., Sepúlveda N., Strand E.B., Pheby D., Alegre J., et al. European Network on ME/CFS (EUROMENE). Prevalence and incidence of myalgic encephalomyelitis/chronic fatigue syndrome in Europe: The Euro-EpiME study from the European network on ME/CFS (EUROMENE): A protocol for a systematic review. BMJ Open. 2018;8:e020817. doi: 10.1136/bmjopen-2017-020817.
    1. Clayton E.W. Beyond myalgic encephalomyelitis/chronic fatigue syndrome: An IOM report on redefining an illness. JAMA. 2015;313:1101–1102. doi: 10.1001/jama.2015.1346.
    1. Fukuda K., Straus S.E., Hickie I., Sharpe M.C., Dobbins J.G., Komaroff A., Schluederberg A., Jones J.F., Lloyd A.R., Wessely S., et al. The chronic fatigue syndrome: A comprehensive approach to its definition and study. Ann. Intern. Med. 1994;121:953–959. doi: 10.7326/0003-4819-121-12-199412150-00009.
    1. Carruthers B.M., Jain A.K., De Meirleir K.L., Peterson D.L., Klimas N.G., Lerner A., Bested A.C., Flor-Henry P., Joshi P., Powles A.C.P., et al. Myalgic encephalomyelitis/chronic fatigue syndrome: Clinical working case definition, diagnostic and treatment protocols. J. Chron. Fatigue Syndr. 2003;11:7–115. doi: 10.1300/J092v11n01_02.
    1. Carruthers B.M., van de Sande M.I., De Meirleir K.L., Klimas N.G., Broderick G., Mitchell T., Staines D., Powles A.C.P., Speight N., Vallings R., et al. Myalgic encephalomyelitis: International Consensus Criteria. J. Intern. Med. 2011;270:3273–3278. doi: 10.1111/j.1365-2796.2011.02428.x.
    1. Lidbury B.A., Fisher P.R. Biomedical Insights that Inform the Diagnosis of ME/CFS. Diagnostics. 2020;10:92. doi: 10.3390/diagnostics10020092.
    1. Nacul L., de Barros B., Kingdon C.C., Cliff J.M., Clark T.G., Mudie K., Dockrell H.M., Lacerda E.M. Evidence of Clinical Pathology Abnormalities in People with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) from an Analytic Cross-Sectional Study. Diagnostics. 2019;9:41. doi: 10.3390/diagnostics9020041.
    1. Almenar-Pérez E., Sarria L., Nathanson L., Oltra E. Assessing diagnostic value of microRNAs from peripheral blood mononuclear cells and extracellular vesicles in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome. Sci. Rep. 2020;10:2064. doi: 10.1038/s41598-020-58506-5.
    1. Kitami T., Fukuda S., Kato T., Yamaguti K., Nakatomi Y., Yamano E., Kataoka Y., Mizuno K., Tsuboi Y., Kogo Y., et al. Deep phenotyping of myalgic encephalomyelitis/chronic fatigue syndrome in Japanese population. Sci. Rep. 2020;10:19933. doi: 10.1038/s41598-020-77105-y.
    1. Fisk J.D., Ritvo P.G., Ross L., Haase D.A., Marrie T.J., Schlech W.F. Measuring the functional impact of fatigue: Initial validation of the fatigue impact scale. Clin. Infect. Dis. 1994;18:S79–S83. doi: 10.1093/clinids/18.Supplement_1.S79.
    1. Sletten D.M., Suarez G.A., Low P.A., Mandrekar J., Singer W. COMPASS 31: A refined and abbreviated Composite Autonomic Symptom Score. Mayo. Clin. Proc. 2012;87:1196–1201. doi: 10.1016/j.mayocp.2012.10.013.
    1. Buysse D.J., Reynolds C.F., Monk T.H., Berman S.R., Kupfer D.J. The Pittsburgh Sleep Quality Index: A new instrument for psychiatric practice and research. Psychiatry Res. 1989;28:193–213. doi: 10.1016/0165-1781(89)90047-4.
    1. Alonso J., Prieto L., Antó J.M. The Spanish version of the SF-36 Health Survey: An instrument for measuring clinical results. Med. Clin. 1995;104:771–776.
    1. Ward J.H., Jr. Hierarchical Grouping to Optimize an Objective Function. J. Am. Stat. Assoc. 1963;58:236–244. doi: 10.1080/01621459.1963.10500845.
    1. R Core Team . R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; Vienna, Austria: 2019. [(accessed on 4 September 2020)]. Available online: .
    1. Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer; New York, NY, USA: 2016.
    1. Obese Chooi Y.C., Ding C., Magkos F. The epidemiology of obesity. Metabolism. 2019;92:6–10. doi: 10.1016/j.metabol.2018.09.005.
    1. Ferrari D., Lombardi G., Banfi G. Concerning the vitamin D reference range: Pre-analytical and analytical variability of vitamin D measurement. Biochem. Med. 2017;27:030501. doi: 10.11613/BM.2017.030501.
    1. Nikolac Gabaj N., Unic A., Miler M., Pavicic T., Culej J., Bolanca I., Herman Mahecic D., Milevoj Kopcinovic L., Vrtaric A. In sickness and in health: Pivotal role of vitamin D. Biochem Med. 2020;30:020501. doi: 10.11613/BM.2020.020501.
    1. Surdu A.M., Pînzariu O., Ciobanu D.M., Negru A.G., Căinap S.S., Lazea C., Iacob D., Săraci G., Tirinescu D., Borda I.M., et al. Vitamin D and Its Role in the Lipid Metabolism and the Development of Atherosclerosis. Biomedicines. 2021;9:172. doi: 10.3390/biomedicines9020172.
    1. Yarparvar A., Elmadfa I., Djazayery A., Abdollahi Z., Salehi F. The Association of Vitamin D Status with Lipid Profile and Inflammation Biomarkers in Healthy Adolescents. Nutrients. 2020;12:590. doi: 10.3390/nu12020590.
    1. Trendelenburg M. Autoantibodies against complement component C1q in systemic lupus erythematosus. Clin. Transl. Immunol. 2021;10:e1279. doi: 10.1002/cti2.1279.
    1. Słomko J., Estévez-López F., Kujawski S., Zawadka-Kunikowska M., Tafil-Klawe M., Klawe J.J., Morten K.J., Szrajda J., Murovska M., Newton J.L., et al. Autonomic Phenotypes in Chronic Fatigue Syndrome Are Associated with Illness Severity: A Cluster Analysis. J. Clin. Med. 2020;9:2531. doi: 10.3390/jcm9082531.
    1. Merle N.S., Church S.E., Fremeaux-Bacchi V., Roumenina L.T. Complement System Part I—Molecular Mechanisms of Activation and Regulation. Front Immunol. 2015;6:262. doi: 10.3389/fimmu.2015.00262.
    1. Merle N.S., Noe R., Halbwachs-Mecarelli L., Fremeaux-Bacchi V., Roumenina L.T. Complement System Part II: Role in Immunity. Front Immunol. 2015;6:257. doi: 10.3389/fimmu.2015.00257.
    1. Roumenina L.T., Popov K.T., Bureeva S.V., Kojouharova M., Gadjeva M., Rabheru S., Thakrar R., Kaplun A., Kishore U. Interaction of the globular domain of human C1q with Salmonella typhimurium lipopolysaccharide. Biochim. Biophys. Acta. 2008;1784:1271–1276. doi: 10.1016/j.bbapap.2008.04.029.
    1. Gaboriaud C., Frachet P., Thielens N.M., Arlaud G.J. The human c1q globular domain: Structure and recognition of non-immune self-ligands. Front Immunol. 2012;2:92. doi: 10.3389/fimmu.2011.00092.
    1. Païdassi H., Tacnet-Delorme P., Lunardi T., Arlaud G.J., Thielens N.M., Frachet P. The lectin-like activity of human C1q and its implication in DNA and apoptotic cell recognition. FEBS Lett. 2008;582:3111–3116. doi: 10.1016/j.febslet.2008.08.001.
    1. Defendi F., Thielens N.M., Clavarino G., Cesbron J.Y., Dumestre-Pérard C. The Immunopathology of Complement Proteins and Innate Immunity in Autoimmune Disease. Clin. Rev. Allergy Immunol. 2020;58:229–251. doi: 10.1007/s12016-019-08774-5.
    1. Morris G., Berk M., Galecki P., Maes M. The emerging role of autoimmunity in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) Mol Neurobiol. 2014;49:741–756. doi: 10.1007/s12035-013-8553-0.
    1. Morris G., Berk M., Klein H., Walder K., Galecki P., Maes M. Nitrosative Stress, Hypernitrosylation, and Autoimmune Responses to Nitrosylated Proteins: New Pathways in Neuroprogressive Disorders Including Depression and Chronic Fatigue Syndrome. Mol. Neurobiol. 2017;54:4271–4291. doi: 10.1007/s12035-016-9975-2.
    1. Benavente F., Piltti K.M., Hooshmand M.J., Nava A.A., Lakatos A., Feld B.G., Creasman D., Gershon P.D., Anderson A. Novel C1q receptor-mediated signaling controls neural stem cell behavior and neurorepair. Elife. 2020;9:e55732. doi: 10.7554/eLife.55732.
    1. Noble M., Pröschel C. The many roles of C1q. Elife. 2020;9:e61599. doi: 10.7554/eLife.61599.
    1. Kouser L., Madhukaran S.P., Shastri A., Saraon A., Ferluga J., Al-Mozaini M., Kishore U. Emerging and Novel Functions of Complement Protein C1q. Front Immunol. 2015;6:317. doi: 10.3389/fimmu.2015.00317.
    1. Cho K. Emerging Roles of Complement Protein C1q in Neurodegeneration. Aging Dis. 2019;10:652–663. doi: 10.14336/AD.2019.0118.
    1. Bialas A.R., Stevens B. TGF-β signaling regulates neuronal C1q expression and developmental synaptic refinement. Nat. Neurosci. 2013;16:1773–1782. doi: 10.1038/nn.3560.
    1. Färber K., Cheung G., Mitchell D., Wallis R., Weihe E., Schwaeble W., Kettenmann H. C1q, the recognition subcomponent of the classical pathway of complement, drives microglial activation. J. Neurosci. Res. 2009;87:644–652. doi: 10.1002/jnr.21875.
    1. Lynch N.J., Willis C.L., Nolan C.C., Roscher S., Fowler M.J., Weihe E., Ray D.E., Schwaeble W.J. Microglial activation and increased synthesis of complement component C1q precedes blood-brain barrier dysfunction in rats. Mol. Immunol. 2004;40:709–716. doi: 10.1016/j.molimm.2003.08.009.
    1. Burckhardt C.S., Clark S.R., Bennett R.M. The fibromyalgia impact questionnaire: Development and validation. J. Rheumatol. 1991;18:728–733.
    1. Rivera J., González T. The Fibromyalgia Impact Questionnaire: A validated Spanish version to assess the health status in women with fibromyalgia. Clin. Exp. Rheumatol. 2004;22:554–560.
    1. Patel K.V., Amtmann D., Jensen M.P., Smith S.M., Veasley C., Turk D.C. Clinical outcome assessment in clinical trials of chronic pain treatments. Pain Rep. 2021;6:e784. doi: 10.1097/PR9.0000000000000784.
    1. Holick M.F., Binkley N.C., Bischoff-Ferrari H.A., Gordon C.M., Hanley D.A., Heaney R.P., Murad M.H., Weaver C.M. Evaluation, treatment, and prevention of vitamin D deficiency: An Endocrine Society clinical practice guideline. J. Clin. Endocrinol. Metab. 2011;96:1911–1930. doi: 10.1210/jc.2011-0385.
    1. Ross A.C., Manson J.E., Abrams S.A., Aloia J.F., Brannon P.M., Clinton S.K., Durazo-Arvizu R.A., Gallagher J.C., Gallo R.L., Jones G., et al. The 2011 report on dietary reference intakes for calcium and vitamin D from the Institute of Medicine: What clinicians need to know. J. Clin. Endocrinol. Metab. 2011;96:53–58. doi: 10.1210/jc.2010-2704.

Source: PubMed

Patrocinadores e Colaboradores

Condições médicas

Intervenções de drogas

3
Se inscrever