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Complement Component C1q as a Potential Diagnostic Tool for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome
Jesús Castro-Marrero1, Mario Zacares2, Eloy Almenar-Pérez3
1ME/CFS Research Unit, Division of Rheumatology, Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona, 08035 Barcelona, Spain.
Insights
This study identified three ME/CFS symptom clusters using blood analytics. High C1q levels may indicate a subgroup with more pain, aiding future diagnosis and treatment for myalgic encephalomyelitis/chronic fatigue syndrome.
Area of Science:
- Immunology
- Hematology
- Clinical Diagnostics
Background:
- Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) diagnosis relies solely on clinical symptoms.
- Blood tests currently only exclude other conditions causing fatigue.
- Limited research exists on comprehensive blood data analysis for ME/CFS subtyping.
Purpose of the Study:
- To explore ME/CFS case subgroups using unbiased cluster analysis of blood data.
- To identify potential blood biomarkers for ME/CFS classification.
Main Methods:
- Hierarchical cluster analysis applied to a cohort of 250 phenotyped female ME/CFS patients.
- Analysis of extensive blood datasets to identify patterns and correlations.
Main Results:
- Three distinct symptom clusters (severe, moderate, mild) were identified.
- Significant differences in five blood parameters were observed across clusters (p < 0.05).
- Elevated circulating complement factor C1q found in 43% of participants, correlating with increased pain symptoms.
Conclusions:
- Blood analytics can potentially stratify ME/CFS patients.
- Complement factor C1q may serve as a biomarker for a specific ME/CFS subgroup.
- Findings suggest implications for ME/CFS etiology research, diagnosis, and treatment development.
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.
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