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. 2021 Sep 15;10(18):4171.
doi: 10.3390/jcm10184171.

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

Jesús Castro-Marrero  1 , Mario Zacares  2 , Eloy Almenar-Pérez  3 , José Alegre-Martín  4 , Elisa Oltra  3   5
Affiliations

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

Jesús Castro-Marrero et al. J Clin Med. .

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.

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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.
See this image and copyright information in PMC

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